
for ds_name in datasets.keys():
    print(f'dataset {ds_name:24}: [ {datasets[ds_name].shape[0]:10,}, {datasets[ds_name].shape[1]}]')

dataset application_train       : [    307,511, 122]
dataset application_test        : [     48,744, 121]
dataset bureau                  : [  1,716,428, 17]
dataset bureau_balance          : [ 27,299,925, 3]
dataset credit_card_balance     : [  3,840,312, 23]
dataset installments_payments   : [ 13,605,401, 8]
dataset previous_application    : [  1,670,214, 37]
dataset POS_CASH_balance        : [  3,829,580, 8]


def plot_missing_data(df, x, y):
    g = sns.displot(
        data=datasets[df].isna().melt(value_name="missing"),
        y="variable",
        hue="missing",
        multiple="fill",
        aspect=1.25
    )
    g.fig.set_figwidth(x)
    g.fig.set_figheight(y)


datasets["application_train"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB


datasets["application_train"].columns

Index(['SK_ID_CURR', 'TARGET', 'NAME_CONTRACT_TYPE', 'CODE_GENDER',
       'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',
       'AMT_CREDIT', 'AMT_ANNUITY',
       ...
       'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20',
       'FLAG_DOCUMENT_21', 'AMT_REQ_CREDIT_BUREAU_HOUR',
       'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
       'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',
       'AMT_REQ_CREDIT_BUREAU_YEAR'],
      dtype='object', length=122)


datasets["application_train"].dtypes

SK_ID_CURR                      int64
TARGET                          int64
NAME_CONTRACT_TYPE             object
CODE_GENDER                    object
FLAG_OWN_CAR                   object
                               ...   
AMT_REQ_CREDIT_BUREAU_DAY     float64
AMT_REQ_CREDIT_BUREAU_WEEK    float64
AMT_REQ_CREDIT_BUREAU_MON     float64
AMT_REQ_CREDIT_BUREAU_QRT     float64
AMT_REQ_CREDIT_BUREAU_YEAR    float64
Length: 122, dtype: object


datasets["application_train"].describe() #numerical only features


datasets["application_train"].describe(include='all')


datasets["application_train"].corr()


percent = (datasets["application_train"].isnull().sum()/datasets["application_train"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["application_train"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("application_train",18,20)


datasets["application_test"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 48744 entries, 0 to 48743
Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(40), object(16)
memory usage: 45.0+ MB


datasets["application_test"].columns

Index(['SK_ID_CURR', 'NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR',
       'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT',
       'AMT_ANNUITY', 'AMT_GOODS_PRICE',
       ...
       'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20',
       'FLAG_DOCUMENT_21', 'AMT_REQ_CREDIT_BUREAU_HOUR',
       'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
       'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',
       'AMT_REQ_CREDIT_BUREAU_YEAR'],
      dtype='object', length=121)


datasets["application_test"].dtypes

SK_ID_CURR                      int64
NAME_CONTRACT_TYPE             object
CODE_GENDER                    object
FLAG_OWN_CAR                   object
FLAG_OWN_REALTY                object
                               ...   
AMT_REQ_CREDIT_BUREAU_DAY     float64
AMT_REQ_CREDIT_BUREAU_WEEK    float64
AMT_REQ_CREDIT_BUREAU_MON     float64
AMT_REQ_CREDIT_BUREAU_QRT     float64
AMT_REQ_CREDIT_BUREAU_YEAR    float64
Length: 121, dtype: object


datasets["application_test"].describe() #numerical only features


datasets["application_test"].describe(include='all') #look at all categorical and numerical


datasets["application_test"].corr()


percent = (datasets["application_test"].isnull().sum()/datasets["application_test"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["application_test"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("application_test",18,20)


datasets["bureau"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1716428 entries, 0 to 1716427
Data columns (total 17 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_CURR              int64  
 1   SK_ID_BUREAU            int64  
 2   CREDIT_ACTIVE           object 
 3   CREDIT_CURRENCY         object 
 4   DAYS_CREDIT             int64  
 5   CREDIT_DAY_OVERDUE      int64  
 6   DAYS_CREDIT_ENDDATE     float64
 7   DAYS_ENDDATE_FACT       float64
 8   AMT_CREDIT_MAX_OVERDUE  float64
 9   CNT_CREDIT_PROLONG      int64  
 10  AMT_CREDIT_SUM          float64
 11  AMT_CREDIT_SUM_DEBT     float64
 12  AMT_CREDIT_SUM_LIMIT    float64
 13  AMT_CREDIT_SUM_OVERDUE  float64
 14  CREDIT_TYPE             object 
 15  DAYS_CREDIT_UPDATE      int64  
 16  AMT_ANNUITY             float64
dtypes: float64(8), int64(6), object(3)
memory usage: 222.6+ MB


datasets["bureau"].columns

Index(['SK_ID_CURR', 'SK_ID_BUREAU', 'CREDIT_ACTIVE', 'CREDIT_CURRENCY',
       'DAYS_CREDIT', 'CREDIT_DAY_OVERDUE', 'DAYS_CREDIT_ENDDATE',
       'DAYS_ENDDATE_FACT', 'AMT_CREDIT_MAX_OVERDUE', 'CNT_CREDIT_PROLONG',
       'AMT_CREDIT_SUM', 'AMT_CREDIT_SUM_DEBT', 'AMT_CREDIT_SUM_LIMIT',
       'AMT_CREDIT_SUM_OVERDUE', 'CREDIT_TYPE', 'DAYS_CREDIT_UPDATE',
       'AMT_ANNUITY'],
      dtype='object')


datasets["bureau"].dtypes

SK_ID_CURR                  int64
SK_ID_BUREAU                int64
CREDIT_ACTIVE              object
CREDIT_CURRENCY            object
DAYS_CREDIT                 int64
CREDIT_DAY_OVERDUE          int64
DAYS_CREDIT_ENDDATE       float64
DAYS_ENDDATE_FACT         float64
AMT_CREDIT_MAX_OVERDUE    float64
CNT_CREDIT_PROLONG          int64
AMT_CREDIT_SUM            float64
AMT_CREDIT_SUM_DEBT       float64
AMT_CREDIT_SUM_LIMIT      float64
AMT_CREDIT_SUM_OVERDUE    float64
CREDIT_TYPE                object
DAYS_CREDIT_UPDATE          int64
AMT_ANNUITY               float64
dtype: object


datasets["bureau"].describe()


datasets["bureau"].describe(include='all')


datasets["bureau"].corr()


percent = (datasets["bureau"].isnull().sum()/datasets["bureau"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["bureau"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("bureau",18,20)


datasets["bureau_balance"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27299925 entries, 0 to 27299924
Data columns (total 3 columns):
 #   Column          Dtype 
---  ------          ----- 
 0   SK_ID_BUREAU    int64 
 1   MONTHS_BALANCE  int64 
 2   STATUS          object
dtypes: int64(2), object(1)
memory usage: 624.8+ MB


datasets["bureau_balance"].columns

Index(['SK_ID_BUREAU', 'MONTHS_BALANCE', 'STATUS'], dtype='object')


datasets["bureau_balance"].dtypes

SK_ID_BUREAU       int64
MONTHS_BALANCE     int64
STATUS            object
dtype: object


datasets["bureau_balance"].describe()


datasets["bureau_balance"].describe(include='all')


datasets["bureau_balance"].corr()


percent = (datasets["bureau_balance"].isnull().sum()/datasets["bureau_balance"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["bureau_balance"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("bureau_balance",18,20)


datasets["POS_CASH_balance"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3829580 entries, 0 to 3829579
Data columns (total 8 columns):
 #   Column                 Dtype  
---  ------                 -----  
 0   SK_ID_PREV             int64  
 1   SK_ID_CURR             int64  
 2   MONTHS_BALANCE         int64  
 3   CNT_INSTALMENT         float64
 4   CNT_INSTALMENT_FUTURE  float64
 5   NAME_CONTRACT_STATUS   object 
 6   SK_DPD                 float64
 7   SK_DPD_DEF             float64
dtypes: float64(4), int64(3), object(1)
memory usage: 233.7+ MB


datasets["POS_CASH_balance"].columns

Index(['SK_ID_PREV', 'SK_ID_CURR', 'MONTHS_BALANCE', 'CNT_INSTALMENT',
       'CNT_INSTALMENT_FUTURE', 'NAME_CONTRACT_STATUS', 'SK_DPD',
       'SK_DPD_DEF'],
      dtype='object')


datasets["POS_CASH_balance"].dtypes

SK_ID_PREV                 int64
SK_ID_CURR                 int64
MONTHS_BALANCE             int64
CNT_INSTALMENT           float64
CNT_INSTALMENT_FUTURE    float64
NAME_CONTRACT_STATUS      object
SK_DPD                   float64
SK_DPD_DEF               float64
dtype: object


datasets["POS_CASH_balance"].describe()


datasets["POS_CASH_balance"].describe(include='all')


datasets["POS_CASH_balance"].corr()


percent = (datasets["POS_CASH_balance"].isnull().sum()/datasets["POS_CASH_balance"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["POS_CASH_balance"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("POS_CASH_balance",18,20)


datasets["credit_card_balance"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3840312 entries, 0 to 3840311
Data columns (total 23 columns):
 #   Column                      Dtype  
---  ------                      -----  
 0   SK_ID_PREV                  int64  
 1   SK_ID_CURR                  int64  
 2   MONTHS_BALANCE              int64  
 3   AMT_BALANCE                 float64
 4   AMT_CREDIT_LIMIT_ACTUAL     int64  
 5   AMT_DRAWINGS_ATM_CURRENT    float64
 6   AMT_DRAWINGS_CURRENT        float64
 7   AMT_DRAWINGS_OTHER_CURRENT  float64
 8   AMT_DRAWINGS_POS_CURRENT    float64
 9   AMT_INST_MIN_REGULARITY     float64
 10  AMT_PAYMENT_CURRENT         float64
 11  AMT_PAYMENT_TOTAL_CURRENT   float64
 12  AMT_RECEIVABLE_PRINCIPAL    float64
 13  AMT_RECIVABLE               float64
 14  AMT_TOTAL_RECEIVABLE        float64
 15  CNT_DRAWINGS_ATM_CURRENT    float64
 16  CNT_DRAWINGS_CURRENT        int64  
 17  CNT_DRAWINGS_OTHER_CURRENT  float64
 18  CNT_DRAWINGS_POS_CURRENT    float64
 19  CNT_INSTALMENT_MATURE_CUM   float64
 20  NAME_CONTRACT_STATUS        object 
 21  SK_DPD                      int64  
 22  SK_DPD_DEF                  int64  
dtypes: float64(15), int64(7), object(1)
memory usage: 673.9+ MB


datasets["credit_card_balance"].columns

Index(['SK_ID_PREV', 'SK_ID_CURR', 'MONTHS_BALANCE', 'AMT_BALANCE',
       'AMT_CREDIT_LIMIT_ACTUAL', 'AMT_DRAWINGS_ATM_CURRENT',
       'AMT_DRAWINGS_CURRENT', 'AMT_DRAWINGS_OTHER_CURRENT',
       'AMT_DRAWINGS_POS_CURRENT', 'AMT_INST_MIN_REGULARITY',
       'AMT_PAYMENT_CURRENT', 'AMT_PAYMENT_TOTAL_CURRENT',
       'AMT_RECEIVABLE_PRINCIPAL', 'AMT_RECIVABLE', 'AMT_TOTAL_RECEIVABLE',
       'CNT_DRAWINGS_ATM_CURRENT', 'CNT_DRAWINGS_CURRENT',
       'CNT_DRAWINGS_OTHER_CURRENT', 'CNT_DRAWINGS_POS_CURRENT',
       'CNT_INSTALMENT_MATURE_CUM', 'NAME_CONTRACT_STATUS', 'SK_DPD',
       'SK_DPD_DEF'],
      dtype='object')


datasets["credit_card_balance"].dtypes

SK_ID_PREV                      int64
SK_ID_CURR                      int64
MONTHS_BALANCE                  int64
AMT_BALANCE                   float64
AMT_CREDIT_LIMIT_ACTUAL         int64
AMT_DRAWINGS_ATM_CURRENT      float64
AMT_DRAWINGS_CURRENT          float64
AMT_DRAWINGS_OTHER_CURRENT    float64
AMT_DRAWINGS_POS_CURRENT      float64
AMT_INST_MIN_REGULARITY       float64
AMT_PAYMENT_CURRENT           float64
AMT_PAYMENT_TOTAL_CURRENT     float64
AMT_RECEIVABLE_PRINCIPAL      float64
AMT_RECIVABLE                 float64
AMT_TOTAL_RECEIVABLE          float64
CNT_DRAWINGS_ATM_CURRENT      float64
CNT_DRAWINGS_CURRENT            int64
CNT_DRAWINGS_OTHER_CURRENT    float64
CNT_DRAWINGS_POS_CURRENT      float64
CNT_INSTALMENT_MATURE_CUM     float64
NAME_CONTRACT_STATUS           object
SK_DPD                          int64
SK_DPD_DEF                      int64
dtype: object


datasets["credit_card_balance"].describe()


datasets["credit_card_balance"].describe(include='all')


datasets["credit_card_balance"].corr()


percent = (datasets["credit_card_balance"].isnull().sum()/datasets["credit_card_balance"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["credit_card_balance"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("credit_card_balance",18,20)


datasets["previous_application"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB


datasets["previous_application"].columns

Index(['SK_ID_PREV', 'SK_ID_CURR', 'NAME_CONTRACT_TYPE', 'AMT_ANNUITY',
       'AMT_APPLICATION', 'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE',
       'WEEKDAY_APPR_PROCESS_START', 'HOUR_APPR_PROCESS_START',
       'FLAG_LAST_APPL_PER_CONTRACT', 'NFLAG_LAST_APPL_IN_DAY',
       'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
       'RATE_INTEREST_PRIVILEGED', 'NAME_CASH_LOAN_PURPOSE',
       'NAME_CONTRACT_STATUS', 'DAYS_DECISION', 'NAME_PAYMENT_TYPE',
       'CODE_REJECT_REASON', 'NAME_TYPE_SUITE', 'NAME_CLIENT_TYPE',
       'NAME_GOODS_CATEGORY', 'NAME_PORTFOLIO', 'NAME_PRODUCT_TYPE',
       'CHANNEL_TYPE', 'SELLERPLACE_AREA', 'NAME_SELLER_INDUSTRY',
       'CNT_PAYMENT', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION',
       'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION',
       'DAYS_LAST_DUE', 'DAYS_TERMINATION', 'NFLAG_INSURED_ON_APPROVAL'],
      dtype='object')


datasets["previous_application"].dtypes

SK_ID_PREV                       int64
SK_ID_CURR                       int64
NAME_CONTRACT_TYPE              object
AMT_ANNUITY                    float64
AMT_APPLICATION                float64
AMT_CREDIT                     float64
AMT_DOWN_PAYMENT               float64
AMT_GOODS_PRICE                float64
WEEKDAY_APPR_PROCESS_START      object
HOUR_APPR_PROCESS_START          int64
FLAG_LAST_APPL_PER_CONTRACT     object
NFLAG_LAST_APPL_IN_DAY           int64
RATE_DOWN_PAYMENT              float64
RATE_INTEREST_PRIMARY          float64
RATE_INTEREST_PRIVILEGED       float64
NAME_CASH_LOAN_PURPOSE          object
NAME_CONTRACT_STATUS            object
DAYS_DECISION                    int64
NAME_PAYMENT_TYPE               object
CODE_REJECT_REASON              object
NAME_TYPE_SUITE                 object
NAME_CLIENT_TYPE                object
NAME_GOODS_CATEGORY             object
NAME_PORTFOLIO                  object
NAME_PRODUCT_TYPE               object
CHANNEL_TYPE                    object
SELLERPLACE_AREA                 int64
NAME_SELLER_INDUSTRY            object
CNT_PAYMENT                    float64
NAME_YIELD_GROUP                object
PRODUCT_COMBINATION             object
DAYS_FIRST_DRAWING             float64
DAYS_FIRST_DUE                 float64
DAYS_LAST_DUE_1ST_VERSION      float64
DAYS_LAST_DUE                  float64
DAYS_TERMINATION               float64
NFLAG_INSURED_ON_APPROVAL      float64
dtype: object


datasets["previous_application"].describe()


datasets["previous_application"].describe(include='all')


datasets["previous_application"].corr()


percent = (datasets["previous_application"].isnull().sum()/datasets["previous_application"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["previous_application"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("previous_application",18,20)


datasets["installments_payments"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13605401 entries, 0 to 13605400
Data columns (total 8 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_PREV              int64  
 1   SK_ID_CURR              int64  
 2   NUM_INSTALMENT_VERSION  float64
 3   NUM_INSTALMENT_NUMBER   int64  
 4   DAYS_INSTALMENT         float64
 5   DAYS_ENTRY_PAYMENT      float64
 6   AMT_INSTALMENT          float64
 7   AMT_PAYMENT             float64
dtypes: float64(5), int64(3)
memory usage: 830.4 MB


datasets["installments_payments"].columns

Index(['SK_ID_PREV', 'SK_ID_CURR', 'NUM_INSTALMENT_VERSION',
       'NUM_INSTALMENT_NUMBER', 'DAYS_INSTALMENT', 'DAYS_ENTRY_PAYMENT',
       'AMT_INSTALMENT', 'AMT_PAYMENT'],
      dtype='object')


datasets["installments_payments"].dtypes

SK_ID_PREV                  int64
SK_ID_CURR                  int64
NUM_INSTALMENT_VERSION    float64
NUM_INSTALMENT_NUMBER       int64
DAYS_INSTALMENT           float64
DAYS_ENTRY_PAYMENT        float64
AMT_INSTALMENT            float64
AMT_PAYMENT               float64
dtype: object


datasets["installments_payments"].describe()


datasets["installments_payments"].describe(include='all')


datasets["installments_payments"].corr()


percent = (datasets["installments_payments"].isnull().sum()/datasets["installments_payments"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["installments_payments"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


for ds_name in datasets.keys():
    print(f'dataset {ds_name:24}: [ {datasets[ds_name].shape[0]:10,}, {datasets[ds_name].shape[1]}]')

dataset application_train       : [    307,511, 122]
dataset application_test        : [     48,744, 121]
dataset bureau                  : [  1,716,428, 17]
dataset bureau_balance          : [ 27,299,925, 3]
dataset credit_card_balance     : [  3,840,312, 23]
dataset installments_payments   : [ 13,605,401, 8]
dataset previous_application    : [  1,670,214, 37]
dataset POS_CASH_balance        : [  3,829,580, 8]


def plot_missing_data(df, x, y):
    g = sns.displot(
        data=datasets[df].isna().melt(value_name="missing"),
        y="variable",
        hue="missing",
        multiple="fill",
        aspect=1.25
    )
    g.fig.set_figwidth(x)
    g.fig.set_figheight(y)


datasets["application_train"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB


datasets["application_train"].columns

Index(['SK_ID_CURR', 'TARGET', 'NAME_CONTRACT_TYPE', 'CODE_GENDER',
       'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',
       'AMT_CREDIT', 'AMT_ANNUITY',
       ...
       'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20',
       'FLAG_DOCUMENT_21', 'AMT_REQ_CREDIT_BUREAU_HOUR',
       'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
       'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',
       'AMT_REQ_CREDIT_BUREAU_YEAR'],
      dtype='object', length=122)


datasets["application_train"].dtypes

SK_ID_CURR                      int64
TARGET                          int64
NAME_CONTRACT_TYPE             object
CODE_GENDER                    object
FLAG_OWN_CAR                   object
                               ...   
AMT_REQ_CREDIT_BUREAU_DAY     float64
AMT_REQ_CREDIT_BUREAU_WEEK    float64
AMT_REQ_CREDIT_BUREAU_MON     float64
AMT_REQ_CREDIT_BUREAU_QRT     float64
AMT_REQ_CREDIT_BUREAU_YEAR    float64
Length: 122, dtype: object


datasets["application_train"].describe() #numerical only features


datasets["application_train"].describe(include='all')


datasets["application_train"].corr()


percent = (datasets["application_train"].isnull().sum()/datasets["application_train"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["application_train"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("application_train",18,20)


datasets["application_test"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 48744 entries, 0 to 48743
Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(40), object(16)
memory usage: 45.0+ MB


datasets["application_test"].columns

Index(['SK_ID_CURR', 'NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR',
       'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT',
       'AMT_ANNUITY', 'AMT_GOODS_PRICE',
       ...
       'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20',
       'FLAG_DOCUMENT_21', 'AMT_REQ_CREDIT_BUREAU_HOUR',
       'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
       'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',
       'AMT_REQ_CREDIT_BUREAU_YEAR'],
      dtype='object', length=121)


datasets["application_test"].dtypes

SK_ID_CURR                      int64
NAME_CONTRACT_TYPE             object
CODE_GENDER                    object
FLAG_OWN_CAR                   object
FLAG_OWN_REALTY                object
                               ...   
AMT_REQ_CREDIT_BUREAU_DAY     float64
AMT_REQ_CREDIT_BUREAU_WEEK    float64
AMT_REQ_CREDIT_BUREAU_MON     float64
AMT_REQ_CREDIT_BUREAU_QRT     float64
AMT_REQ_CREDIT_BUREAU_YEAR    float64
Length: 121, dtype: object


datasets["application_test"].describe() #numerical only features


datasets["application_test"].describe(include='all') #look at all categorical and numerical


datasets["application_test"].corr()


percent = (datasets["application_test"].isnull().sum()/datasets["application_test"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["application_test"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("application_test",18,20)


datasets["bureau"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1716428 entries, 0 to 1716427
Data columns (total 17 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_CURR              int64  
 1   SK_ID_BUREAU            int64  
 2   CREDIT_ACTIVE           object 
 3   CREDIT_CURRENCY         object 
 4   DAYS_CREDIT             int64  
 5   CREDIT_DAY_OVERDUE      int64  
 6   DAYS_CREDIT_ENDDATE     float64
 7   DAYS_ENDDATE_FACT       float64
 8   AMT_CREDIT_MAX_OVERDUE  float64
 9   CNT_CREDIT_PROLONG      int64  
 10  AMT_CREDIT_SUM          float64
 11  AMT_CREDIT_SUM_DEBT     float64
 12  AMT_CREDIT_SUM_LIMIT    float64
 13  AMT_CREDIT_SUM_OVERDUE  float64
 14  CREDIT_TYPE             object 
 15  DAYS_CREDIT_UPDATE      int64  
 16  AMT_ANNUITY             float64
dtypes: float64(8), int64(6), object(3)
memory usage: 222.6+ MB


datasets["bureau"].columns

Index(['SK_ID_CURR', 'SK_ID_BUREAU', 'CREDIT_ACTIVE', 'CREDIT_CURRENCY',
       'DAYS_CREDIT', 'CREDIT_DAY_OVERDUE', 'DAYS_CREDIT_ENDDATE',
       'DAYS_ENDDATE_FACT', 'AMT_CREDIT_MAX_OVERDUE', 'CNT_CREDIT_PROLONG',
       'AMT_CREDIT_SUM', 'AMT_CREDIT_SUM_DEBT', 'AMT_CREDIT_SUM_LIMIT',
       'AMT_CREDIT_SUM_OVERDUE', 'CREDIT_TYPE', 'DAYS_CREDIT_UPDATE',
       'AMT_ANNUITY'],
      dtype='object')


datasets["bureau"].dtypes

SK_ID_CURR                  int64
SK_ID_BUREAU                int64
CREDIT_ACTIVE              object
CREDIT_CURRENCY            object
DAYS_CREDIT                 int64
CREDIT_DAY_OVERDUE          int64
DAYS_CREDIT_ENDDATE       float64
DAYS_ENDDATE_FACT         float64
AMT_CREDIT_MAX_OVERDUE    float64
CNT_CREDIT_PROLONG          int64
AMT_CREDIT_SUM            float64
AMT_CREDIT_SUM_DEBT       float64
AMT_CREDIT_SUM_LIMIT      float64
AMT_CREDIT_SUM_OVERDUE    float64
CREDIT_TYPE                object
DAYS_CREDIT_UPDATE          int64
AMT_ANNUITY               float64
dtype: object


datasets["bureau"].describe()


datasets["bureau"].describe(include='all')


datasets["bureau"].corr()


percent = (datasets["bureau"].isnull().sum()/datasets["bureau"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["bureau"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("bureau",18,20)


datasets["bureau_balance"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27299925 entries, 0 to 27299924
Data columns (total 3 columns):
 #   Column          Dtype 
---  ------          ----- 
 0   SK_ID_BUREAU    int64 
 1   MONTHS_BALANCE  int64 
 2   STATUS          object
dtypes: int64(2), object(1)
memory usage: 624.8+ MB


datasets["bureau_balance"].columns

Index(['SK_ID_BUREAU', 'MONTHS_BALANCE', 'STATUS'], dtype='object')


datasets["bureau_balance"].dtypes

SK_ID_BUREAU       int64
MONTHS_BALANCE     int64
STATUS            object
dtype: object


datasets["bureau_balance"].describe()


datasets["bureau_balance"].describe(include='all')


datasets["bureau_balance"].corr()


percent = (datasets["bureau_balance"].isnull().sum()/datasets["bureau_balance"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["bureau_balance"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("bureau_balance",18,20)


datasets["POS_CASH_balance"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3829580 entries, 0 to 3829579
Data columns (total 8 columns):
 #   Column                 Dtype  
---  ------                 -----  
 0   SK_ID_PREV             int64  
 1   SK_ID_CURR             int64  
 2   MONTHS_BALANCE         int64  
 3   CNT_INSTALMENT         float64
 4   CNT_INSTALMENT_FUTURE  float64
 5   NAME_CONTRACT_STATUS   object 
 6   SK_DPD                 float64
 7   SK_DPD_DEF             float64
dtypes: float64(4), int64(3), object(1)
memory usage: 233.7+ MB


datasets["POS_CASH_balance"].columns

Index(['SK_ID_PREV', 'SK_ID_CURR', 'MONTHS_BALANCE', 'CNT_INSTALMENT',
       'CNT_INSTALMENT_FUTURE', 'NAME_CONTRACT_STATUS', 'SK_DPD',
       'SK_DPD_DEF'],
      dtype='object')


datasets["POS_CASH_balance"].dtypes

SK_ID_PREV                 int64
SK_ID_CURR                 int64
MONTHS_BALANCE             int64
CNT_INSTALMENT           float64
CNT_INSTALMENT_FUTURE    float64
NAME_CONTRACT_STATUS      object
SK_DPD                   float64
SK_DPD_DEF               float64
dtype: object


datasets["POS_CASH_balance"].describe()


datasets["POS_CASH_balance"].describe(include='all')


datasets["POS_CASH_balance"].corr()


percent = (datasets["POS_CASH_balance"].isnull().sum()/datasets["POS_CASH_balance"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["POS_CASH_balance"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("POS_CASH_balance",18,20)


datasets["credit_card_balance"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3840312 entries, 0 to 3840311
Data columns (total 23 columns):
 #   Column                      Dtype  
---  ------                      -----  
 0   SK_ID_PREV                  int64  
 1   SK_ID_CURR                  int64  
 2   MONTHS_BALANCE              int64  
 3   AMT_BALANCE                 float64
 4   AMT_CREDIT_LIMIT_ACTUAL     int64  
 5   AMT_DRAWINGS_ATM_CURRENT    float64
 6   AMT_DRAWINGS_CURRENT        float64
 7   AMT_DRAWINGS_OTHER_CURRENT  float64
 8   AMT_DRAWINGS_POS_CURRENT    float64
 9   AMT_INST_MIN_REGULARITY     float64
 10  AMT_PAYMENT_CURRENT         float64
 11  AMT_PAYMENT_TOTAL_CURRENT   float64
 12  AMT_RECEIVABLE_PRINCIPAL    float64
 13  AMT_RECIVABLE               float64
 14  AMT_TOTAL_RECEIVABLE        float64
 15  CNT_DRAWINGS_ATM_CURRENT    float64
 16  CNT_DRAWINGS_CURRENT        int64  
 17  CNT_DRAWINGS_OTHER_CURRENT  float64
 18  CNT_DRAWINGS_POS_CURRENT    float64
 19  CNT_INSTALMENT_MATURE_CUM   float64
 20  NAME_CONTRACT_STATUS        object 
 21  SK_DPD                      int64  
 22  SK_DPD_DEF                  int64  
dtypes: float64(15), int64(7), object(1)
memory usage: 673.9+ MB


datasets["credit_card_balance"].columns

Index(['SK_ID_PREV', 'SK_ID_CURR', 'MONTHS_BALANCE', 'AMT_BALANCE',
       'AMT_CREDIT_LIMIT_ACTUAL', 'AMT_DRAWINGS_ATM_CURRENT',
       'AMT_DRAWINGS_CURRENT', 'AMT_DRAWINGS_OTHER_CURRENT',
       'AMT_DRAWINGS_POS_CURRENT', 'AMT_INST_MIN_REGULARITY',
       'AMT_PAYMENT_CURRENT', 'AMT_PAYMENT_TOTAL_CURRENT',
       'AMT_RECEIVABLE_PRINCIPAL', 'AMT_RECIVABLE', 'AMT_TOTAL_RECEIVABLE',
       'CNT_DRAWINGS_ATM_CURRENT', 'CNT_DRAWINGS_CURRENT',
       'CNT_DRAWINGS_OTHER_CURRENT', 'CNT_DRAWINGS_POS_CURRENT',
       'CNT_INSTALMENT_MATURE_CUM', 'NAME_CONTRACT_STATUS', 'SK_DPD',
       'SK_DPD_DEF'],
      dtype='object')


datasets["credit_card_balance"].dtypes

SK_ID_PREV                      int64
SK_ID_CURR                      int64
MONTHS_BALANCE                  int64
AMT_BALANCE                   float64
AMT_CREDIT_LIMIT_ACTUAL         int64
AMT_DRAWINGS_ATM_CURRENT      float64
AMT_DRAWINGS_CURRENT          float64
AMT_DRAWINGS_OTHER_CURRENT    float64
AMT_DRAWINGS_POS_CURRENT      float64
AMT_INST_MIN_REGULARITY       float64
AMT_PAYMENT_CURRENT           float64
AMT_PAYMENT_TOTAL_CURRENT     float64
AMT_RECEIVABLE_PRINCIPAL      float64
AMT_RECIVABLE                 float64
AMT_TOTAL_RECEIVABLE          float64
CNT_DRAWINGS_ATM_CURRENT      float64
CNT_DRAWINGS_CURRENT            int64
CNT_DRAWINGS_OTHER_CURRENT    float64
CNT_DRAWINGS_POS_CURRENT      float64
CNT_INSTALMENT_MATURE_CUM     float64
NAME_CONTRACT_STATUS           object
SK_DPD                          int64
SK_DPD_DEF                      int64
dtype: object


datasets["credit_card_balance"].describe()


datasets["credit_card_balance"].describe(include='all')


datasets["credit_card_balance"].corr()


percent = (datasets["credit_card_balance"].isnull().sum()/datasets["credit_card_balance"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["credit_card_balance"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("credit_card_balance",18,20)


datasets["previous_application"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB


datasets["previous_application"].columns

Index(['SK_ID_PREV', 'SK_ID_CURR', 'NAME_CONTRACT_TYPE', 'AMT_ANNUITY',
       'AMT_APPLICATION', 'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE',
       'WEEKDAY_APPR_PROCESS_START', 'HOUR_APPR_PROCESS_START',
       'FLAG_LAST_APPL_PER_CONTRACT', 'NFLAG_LAST_APPL_IN_DAY',
       'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
       'RATE_INTEREST_PRIVILEGED', 'NAME_CASH_LOAN_PURPOSE',
       'NAME_CONTRACT_STATUS', 'DAYS_DECISION', 'NAME_PAYMENT_TYPE',
       'CODE_REJECT_REASON', 'NAME_TYPE_SUITE', 'NAME_CLIENT_TYPE',
       'NAME_GOODS_CATEGORY', 'NAME_PORTFOLIO', 'NAME_PRODUCT_TYPE',
       'CHANNEL_TYPE', 'SELLERPLACE_AREA', 'NAME_SELLER_INDUSTRY',
       'CNT_PAYMENT', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION',
       'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION',
       'DAYS_LAST_DUE', 'DAYS_TERMINATION', 'NFLAG_INSURED_ON_APPROVAL'],
      dtype='object')


datasets["previous_application"].dtypes

SK_ID_PREV                       int64
SK_ID_CURR                       int64
NAME_CONTRACT_TYPE              object
AMT_ANNUITY                    float64
AMT_APPLICATION                float64
AMT_CREDIT                     float64
AMT_DOWN_PAYMENT               float64
AMT_GOODS_PRICE                float64
WEEKDAY_APPR_PROCESS_START      object
HOUR_APPR_PROCESS_START          int64
FLAG_LAST_APPL_PER_CONTRACT     object
NFLAG_LAST_APPL_IN_DAY           int64
RATE_DOWN_PAYMENT              float64
RATE_INTEREST_PRIMARY          float64
RATE_INTEREST_PRIVILEGED       float64
NAME_CASH_LOAN_PURPOSE          object
NAME_CONTRACT_STATUS            object
DAYS_DECISION                    int64
NAME_PAYMENT_TYPE               object
CODE_REJECT_REASON              object
NAME_TYPE_SUITE                 object
NAME_CLIENT_TYPE                object
NAME_GOODS_CATEGORY             object
NAME_PORTFOLIO                  object
NAME_PRODUCT_TYPE               object
CHANNEL_TYPE                    object
SELLERPLACE_AREA                 int64
NAME_SELLER_INDUSTRY            object
CNT_PAYMENT                    float64
NAME_YIELD_GROUP                object
PRODUCT_COMBINATION             object
DAYS_FIRST_DRAWING             float64
DAYS_FIRST_DUE                 float64
DAYS_LAST_DUE_1ST_VERSION      float64
DAYS_LAST_DUE                  float64
DAYS_TERMINATION               float64
NFLAG_INSURED_ON_APPROVAL      float64
dtype: object


datasets["previous_application"].describe()


datasets["previous_application"].describe(include='all')


datasets["previous_application"].corr()


percent = (datasets["previous_application"].isnull().sum()/datasets["previous_application"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["previous_application"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


plot_missing_data("previous_application",18,20)


datasets["installments_payments"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13605401 entries, 0 to 13605400
Data columns (total 8 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_PREV              int64  
 1   SK_ID_CURR              int64  
 2   NUM_INSTALMENT_VERSION  float64
 3   NUM_INSTALMENT_NUMBER   int64  
 4   DAYS_INSTALMENT         float64
 5   DAYS_ENTRY_PAYMENT      float64
 6   AMT_INSTALMENT          float64
 7   AMT_PAYMENT             float64
dtypes: float64(5), int64(3)
memory usage: 830.4 MB


datasets["installments_payments"].columns

Index(['SK_ID_PREV', 'SK_ID_CURR', 'NUM_INSTALMENT_VERSION',
       'NUM_INSTALMENT_NUMBER', 'DAYS_INSTALMENT', 'DAYS_ENTRY_PAYMENT',
       'AMT_INSTALMENT', 'AMT_PAYMENT'],
      dtype='object')


datasets["installments_payments"].dtypes

SK_ID_PREV                  int64
SK_ID_CURR                  int64
NUM_INSTALMENT_VERSION    float64
NUM_INSTALMENT_NUMBER       int64
DAYS_INSTALMENT           float64
DAYS_ENTRY_PAYMENT        float64
AMT_INSTALMENT            float64
AMT_PAYMENT               float64
dtype: object


datasets["installments_payments"].describe()


datasets["installments_payments"].describe(include='all')


datasets["installments_payments"].corr()


percent = (datasets["installments_payments"].isnull().sum()/datasets["installments_payments"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["installments_payments"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)


# Import necessary libraries for data preprocessing
import os 
import numpy as np
import pandas as pd 
from sklearn.preprocessing import LabelEncoder
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
from pandas.plotting import scatter_matrix

# Import necessary libraries for data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Import necessary libraries for logistic regression
from sklearn.linear_model import LogisticRegression

# Import necessary libraries for model selection and evaluation
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import auc, accuracy_score, confusion_matrix, f1_score, log_loss, classification_report, roc_auc_score, make_scorer

# Ignore warnings
import warnings
warnings.filterwarnings('ignore')

# Import necessary libraries for building and training neural network
import time
from datetime import datetime
import json
import pickle
import copy

import torch
import tensorflow as tf
import torch.nn as nn
import torch.nn.functional as func
from torch.nn.functional import binary_cross_entropy
import torch.optim as optim
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, BatchNormalization
from tensorflow.keras import layers
from tensorflow.keras.callbacks import LearningRateScheduler


# Import necessary libraries
import time
from datetime import datetime
import json
import pickle
import copy
import warnings

import numpy as np
import pandas as pd 
import torch
import tensorflow as tf
import torch.nn as nn
import torch.nn.functional as func
from torch.nn.functional import binary_cross_entropy
import torch.optim as optim
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from sklearn.preprocessing import LabelEncoder
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline
from sklearn.model_selection import train_test_split, KFold
from sklearn.metrics import auc, accuracy_score, confusion_matrix, f1_score, log_loss, classification_report, roc_auc_score, make_scorer

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, BatchNormalization
from tensorflow.keras import layers
from tensorflow.keras.callbacks import LearningRateScheduler

# Ignore warnings
warnings.filterwarnings('ignore')

# our import script contains code for data preprocessing and a neural network model.


DATA_DIR = "home-credit-default-risk"   #same level as course repo in the data directory
#DATA_DIR = os.path.join('./ddddd/')
#!mkdir DATA_DIR


def load_data(in_path, name):
    df = pd.read_csv(in_path)
    print(f"{name}: shape is {df.shape}")
    print(df.info())
    display(df.head(5))
    return df

datasets={}  # lets store the datasets in a dictionary so we can keep track of them easily
ds_name = 'application_train'
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

datasets['application_train'].shape

application_train: shape is (307511, 122)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB
None

(307511, 122)


ds_name = 'application_test'
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)


ds_name = 'bureau'
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

ds_name = 'previous_application'
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

ds_name = 'installments_payments'
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

application_test: shape is (48744, 121)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 48744 entries, 0 to 48743
Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(40), object(16)
memory usage: 45.0+ MB
None

bureau: shape is (1716428, 17)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1716428 entries, 0 to 1716427
Data columns (total 17 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_CURR              int64  
 1   SK_ID_BUREAU            int64  
 2   CREDIT_ACTIVE           object 
 3   CREDIT_CURRENCY         object 
 4   DAYS_CREDIT             int64  
 5   CREDIT_DAY_OVERDUE      int64  
 6   DAYS_CREDIT_ENDDATE     float64
 7   DAYS_ENDDATE_FACT       float64
 8   AMT_CREDIT_MAX_OVERDUE  float64
 9   CNT_CREDIT_PROLONG      int64  
 10  AMT_CREDIT_SUM          float64
 11  AMT_CREDIT_SUM_DEBT     float64
 12  AMT_CREDIT_SUM_LIMIT    float64
 13  AMT_CREDIT_SUM_OVERDUE  float64
 14  CREDIT_TYPE             object 
 15  DAYS_CREDIT_UPDATE      int64  
 16  AMT_ANNUITY             float64
dtypes: float64(8), int64(6), object(3)
memory usage: 222.6+ MB
None

previous_application: shape is (1670214, 37)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB
None

installments_payments: shape is (13605401, 8)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13605401 entries, 0 to 13605400
Data columns (total 8 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_PREV              int64  
 1   SK_ID_CURR              int64  
 2   NUM_INSTALMENT_VERSION  float64
 3   NUM_INSTALMENT_NUMBER   int64  
 4   DAYS_INSTALMENT         float64
 5   DAYS_ENTRY_PAYMENT      float64
 6   AMT_INSTALMENT          float64
 7   AMT_PAYMENT             float64
dtypes: float64(5), int64(3)
memory usage: 830.4 MB
None


%%time
ds_names = ("application_train", "application_test", "bureau","bureau_balance","credit_card_balance","installments_payments",
            "previous_application","POS_CASH_balance")

for ds_name in ds_names:
    datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

application_train: shape is (307511, 122)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB
None

application_test: shape is (48744, 121)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 48744 entries, 0 to 48743
Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(40), object(16)
memory usage: 45.0+ MB
None

bureau: shape is (1716428, 17)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1716428 entries, 0 to 1716427
Data columns (total 17 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_CURR              int64  
 1   SK_ID_BUREAU            int64  
 2   CREDIT_ACTIVE           object 
 3   CREDIT_CURRENCY         object 
 4   DAYS_CREDIT             int64  
 5   CREDIT_DAY_OVERDUE      int64  
 6   DAYS_CREDIT_ENDDATE     float64
 7   DAYS_ENDDATE_FACT       float64
 8   AMT_CREDIT_MAX_OVERDUE  float64
 9   CNT_CREDIT_PROLONG      int64  
 10  AMT_CREDIT_SUM          float64
 11  AMT_CREDIT_SUM_DEBT     float64
 12  AMT_CREDIT_SUM_LIMIT    float64
 13  AMT_CREDIT_SUM_OVERDUE  float64
 14  CREDIT_TYPE             object 
 15  DAYS_CREDIT_UPDATE      int64  
 16  AMT_ANNUITY             float64
dtypes: float64(8), int64(6), object(3)
memory usage: 222.6+ MB
None

bureau_balance: shape is (27299925, 3)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27299925 entries, 0 to 27299924
Data columns (total 3 columns):
 #   Column          Dtype 
---  ------          ----- 
 0   SK_ID_BUREAU    int64 
 1   MONTHS_BALANCE  int64 
 2   STATUS          object
dtypes: int64(2), object(1)
memory usage: 624.8+ MB
None

credit_card_balance: shape is (3840312, 23)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3840312 entries, 0 to 3840311
Data columns (total 23 columns):
 #   Column                      Dtype  
---  ------                      -----  
 0   SK_ID_PREV                  int64  
 1   SK_ID_CURR                  int64  
 2   MONTHS_BALANCE              int64  
 3   AMT_BALANCE                 float64
 4   AMT_CREDIT_LIMIT_ACTUAL     int64  
 5   AMT_DRAWINGS_ATM_CURRENT    float64
 6   AMT_DRAWINGS_CURRENT        float64
 7   AMT_DRAWINGS_OTHER_CURRENT  float64
 8   AMT_DRAWINGS_POS_CURRENT    float64
 9   AMT_INST_MIN_REGULARITY     float64
 10  AMT_PAYMENT_CURRENT         float64
 11  AMT_PAYMENT_TOTAL_CURRENT   float64
 12  AMT_RECEIVABLE_PRINCIPAL    float64
 13  AMT_RECIVABLE               float64
 14  AMT_TOTAL_RECEIVABLE        float64
 15  CNT_DRAWINGS_ATM_CURRENT    float64
 16  CNT_DRAWINGS_CURRENT        int64  
 17  CNT_DRAWINGS_OTHER_CURRENT  float64
 18  CNT_DRAWINGS_POS_CURRENT    float64
 19  CNT_INSTALMENT_MATURE_CUM   float64
 20  NAME_CONTRACT_STATUS        object 
 21  SK_DPD                      int64  
 22  SK_DPD_DEF                  int64  
dtypes: float64(15), int64(7), object(1)
memory usage: 673.9+ MB
None

installments_payments: shape is (13605401, 8)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13605401 entries, 0 to 13605400
Data columns (total 8 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_PREV              int64  
 1   SK_ID_CURR              int64  
 2   NUM_INSTALMENT_VERSION  float64
 3   NUM_INSTALMENT_NUMBER   int64  
 4   DAYS_INSTALMENT         float64
 5   DAYS_ENTRY_PAYMENT      float64
 6   AMT_INSTALMENT          float64
 7   AMT_PAYMENT             float64
dtypes: float64(5), int64(3)
memory usage: 830.4 MB
None

previous_application: shape is (1670214, 37)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB
None

POS_CASH_balance: shape is (10001358, 8)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10001358 entries, 0 to 10001357
Data columns (total 8 columns):
 #   Column                 Dtype  
---  ------                 -----  
 0   SK_ID_PREV             int64  
 1   SK_ID_CURR             int64  
 2   MONTHS_BALANCE         int64  
 3   CNT_INSTALMENT         float64
 4   CNT_INSTALMENT_FUTURE  float64
 5   NAME_CONTRACT_STATUS   object 
 6   SK_DPD                 int64  
 7   SK_DPD_DEF             int64  
dtypes: float64(2), int64(5), object(1)
memory usage: 610.4+ MB
None

CPU times: user 15.4 s, sys: 2.66 s, total: 18 s
Wall time: 18.3 s


import pandas as pd

def dataset_summary(dataset, summary_type):
    if summary_type == 'info':
        print("")
        print("The Information of ",dataset + " is given below:")
        return(pd.read_csv(dataset).info())
    elif summary_type == 'head':
        print("")
        print("The head of :", dataset + " is given below:")
        return(display(pd.read_csv(dataset).head()))
    elif summary_type == 'tail':
        print("")
        print("The tail of :", dataset + " is given below:")
        return(display(pd.read_csv(dataset).tail()))
    elif summary_type == 'shape':
        print("")
        print("The shape of :", dataset + " is given below:")
        return(display(pd.read_csv(dataset).shape))
    elif summary_type == 'numerical_feat':
        print("")
        print("Below are the numerical features of :", dataset)
        return(display(pd.read_csv(dataset).describe(include = None)))
    elif summary_type == 'categorical_feat':
        print("")
        print("Below are the categorical features of :", dataset)
        return(display(pd.read_csv(dataset).describe(include = 'object')))
    elif summary_type == 'features':
        print("")
        print("Below are the total described features of :", dataset)
        return(display(pd.read_csv(dataset).describe(include = 'all')))
    elif summary_type == 'describe':
        print("")
        print("The decription of :", dataset + " is given below:")
        return(display(pd.read_csv(dataset).describe()))
    elif summary_type == 'datatype_count':
        print("")
        print("The datatype counts of :", dataset + " is given below:")
        return(pd.read_csv(dataset).dtypes.value_counts())
    elif summary_type == 'value_counts':
        print("")
        print("The value count of :", dataset + " is given below:")
        return(display(pd.read_csv(dataset).value_counts))
    else:
        print("Invalid summary_type")


import seaborn as sns
import matplotlib.pyplot as plt
def Missing_Plot(dataset):
    plt.figure(figsize=(210,50))
    sns.displot(
    data=datasets[dataset].iloc[: ,20 :60].isna().melt(value_name="missing"),
    y="variable",
    hue="missing",
    multiple="fill",
    aspect=3
    ).set(title='Missing Values Plot')
Missing_Plot("application_test")

<Figure size 21000x5000 with 0 Axes>


correlations = datasets["application_train"].corr()['TARGET'].sort_values(ascending= True)
print('Most Positive Correlations:\n',correlations.tail(40))
print('\n\n\nMost Negative Correlations:\n',correlations.head(40))

Most Positive Correlations:
 AMT_REQ_CREDIT_BUREAU_QRT     -0.002022
FLAG_EMAIL                    -0.001758
NONLIVINGAPARTMENTS_MODE      -0.001557
FLAG_DOCUMENT_7               -0.001520
FLAG_DOCUMENT_10              -0.001414
FLAG_DOCUMENT_19              -0.001358
FLAG_DOCUMENT_12              -0.000756
FLAG_DOCUMENT_5               -0.000316
FLAG_DOCUMENT_20               0.000215
FLAG_CONT_MOBILE               0.000370
FLAG_MOBIL                     0.000534
AMT_REQ_CREDIT_BUREAU_WEEK     0.000788
AMT_REQ_CREDIT_BUREAU_HOUR     0.000930
AMT_REQ_CREDIT_BUREAU_DAY      0.002704
LIVE_REGION_NOT_WORK_REGION    0.002819
FLAG_DOCUMENT_21               0.003709
FLAG_DOCUMENT_2                0.005417
REG_REGION_NOT_LIVE_REGION     0.005576
REG_REGION_NOT_WORK_REGION     0.006942
OBS_60_CNT_SOCIAL_CIRCLE       0.009022
OBS_30_CNT_SOCIAL_CIRCLE       0.009131
CNT_FAM_MEMBERS                0.009308
CNT_CHILDREN                   0.019187
AMT_REQ_CREDIT_BUREAU_YEAR     0.019930
FLAG_WORK_PHONE                0.028524
DEF_60_CNT_SOCIAL_CIRCLE       0.031276
DEF_30_CNT_SOCIAL_CIRCLE       0.032248
LIVE_CITY_NOT_WORK_CITY        0.032518
OWN_CAR_AGE                    0.037612
DAYS_REGISTRATION              0.041975
FLAG_DOCUMENT_3                0.044346
REG_CITY_NOT_LIVE_CITY         0.044395
FLAG_EMP_PHONE                 0.045982
REG_CITY_NOT_WORK_CITY         0.050994
DAYS_ID_PUBLISH                0.051457
DAYS_LAST_PHONE_CHANGE         0.055218
REGION_RATING_CLIENT           0.058899
REGION_RATING_CLIENT_W_CITY    0.060893
DAYS_BIRTH                     0.078239
TARGET                         1.000000
Name: TARGET, dtype: float64


Most Negative Correlations:
 EXT_SOURCE_3                 -0.178919
EXT_SOURCE_2                 -0.160472
EXT_SOURCE_1                 -0.155317
DAYS_EMPLOYED                -0.044932
FLOORSMAX_AVG                -0.044003
FLOORSMAX_MEDI               -0.043768
FLOORSMAX_MODE               -0.043226
AMT_GOODS_PRICE              -0.039645
REGION_POPULATION_RELATIVE   -0.037227
ELEVATORS_AVG                -0.034199
ELEVATORS_MEDI               -0.033863
FLOORSMIN_AVG                -0.033614
FLOORSMIN_MEDI               -0.033394
LIVINGAREA_AVG               -0.032997
LIVINGAREA_MEDI              -0.032739
FLOORSMIN_MODE               -0.032698
TOTALAREA_MODE               -0.032596
ELEVATORS_MODE               -0.032131
LIVINGAREA_MODE              -0.030685
AMT_CREDIT                   -0.030369
APARTMENTS_AVG               -0.029498
APARTMENTS_MEDI              -0.029184
FLAG_DOCUMENT_6              -0.028602
APARTMENTS_MODE              -0.027284
LIVINGAPARTMENTS_AVG         -0.025031
LIVINGAPARTMENTS_MEDI        -0.024621
HOUR_APPR_PROCESS_START      -0.024166
FLAG_PHONE                   -0.023806
LIVINGAPARTMENTS_MODE        -0.023393
BASEMENTAREA_AVG             -0.022746
YEARS_BUILD_MEDI             -0.022326
YEARS_BUILD_AVG              -0.022149
BASEMENTAREA_MEDI            -0.022081
YEARS_BUILD_MODE             -0.022068
BASEMENTAREA_MODE            -0.019952
ENTRANCES_AVG                -0.019172
ENTRANCES_MEDI               -0.019025
COMMONAREA_MEDI              -0.018573
COMMONAREA_AVG               -0.018550
ENTRANCES_MODE               -0.017387
Name: TARGET, dtype: float64


plt.figure(figsize = (50,50))
corrMap = sns.heatmap(datasets["application_train"].corr(), vmin=-1, vmax = 1, annot=True)


# Correlation map of highly positive correlated features of application train to TARGET
plt.figure(figsize = (50,50))
corr_cols = ['DAYS_BIRTH', 'REGION_RATING_CLIENT_W_CITY','REGION_RATING_CLIENT','DAYS_LAST_PHONE_CHANGE', 'DAYS_ID_PUBLISH',
             'REG_CITY_NOT_WORK_CITY','FLAG_EMP_PHONE','REG_CITY_NOT_LIVE_CITY', 'FLAG_DOCUMENT_3', 'TARGET']
corrMap = sns.heatmap(datasets["application_train"][corr_cols].corr(), vmin=-1, vmax=1, annot=True)


#Applicants Age
plt.hist(datasets["application_train"]['DAYS_BIRTH'] / -365, edgecolor = 'k', bins = 30)
plt.title('Age of Client'); plt.xlabel('Age (years)'); plt.ylabel('Count');

sns.countplot(x='OCCUPATION_TYPE', data=datasets["application_train"], color='Blue');
plt.title('Applicants Occupation');
plt.xticks(rotation=90);


most_corr=datasets["application_train"][['REGION_RATING_CLIENT',
                      'REGION_RATING_CLIENT_W_CITY','DAYS_EMPLOYED','DAYS_BIRTH','TARGET']]
most_corr_corr = most_corr.corr()

sns.set_style("dark")
sns.set_context("notebook", font_scale=2.0, rc={"lines.linewidth": 1.0})
fig, axes = plt.subplots(figsize = (20,10),sharey=True)
sns.heatmap(most_corr_corr,cmap=plt.cm.RdYlBu_r,vmin=-0.25,vmax=0.6,annot=True)
plt.title('Correlation Heatmap for features with highest correlations with target variables')

Text(0.5, 1.0, 'Correlation Heatmap for features with highest correlations with target variables')


import os 

def load_data(in_path, name): 
        df = pd.read_csv(in_path)
        print(f"{name}: shape is {df.shape}") 
        print(df.info())
        display(df.head(5))
        return df
datasets={}
ds_name = 'application_train' 
DATA_DIR=f"/Users/deepak/Desktop/AML/home-credit-default-risk/" 
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)
datasets['application_train'].shape

application_train: shape is (307511, 122)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB
None

(307511, 122)


ds_name = 'application_test'
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

application_test: shape is (48744, 121)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 48744 entries, 0 to 48743
Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(40), object(16)
memory usage: 45.0+ MB
None


%%time
ds_names = ("application_train", "application_test", "bureau","bureau_balance","credit_card_balance","installments_payments",
            "previous_application","POS_CASH_balance")

for ds_name in ds_names:
    datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

application_train: shape is (307511, 122)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB
None

application_test: shape is (48744, 121)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 48744 entries, 0 to 48743
Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(40), object(16)
memory usage: 45.0+ MB
None

bureau: shape is (1716428, 17)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1716428 entries, 0 to 1716427
Data columns (total 17 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_CURR              int64  
 1   SK_ID_BUREAU            int64  
 2   CREDIT_ACTIVE           object 
 3   CREDIT_CURRENCY         object 
 4   DAYS_CREDIT             int64  
 5   CREDIT_DAY_OVERDUE      int64  
 6   DAYS_CREDIT_ENDDATE     float64
 7   DAYS_ENDDATE_FACT       float64
 8   AMT_CREDIT_MAX_OVERDUE  float64
 9   CNT_CREDIT_PROLONG      int64  
 10  AMT_CREDIT_SUM          float64
 11  AMT_CREDIT_SUM_DEBT     float64
 12  AMT_CREDIT_SUM_LIMIT    float64
 13  AMT_CREDIT_SUM_OVERDUE  float64
 14  CREDIT_TYPE             object 
 15  DAYS_CREDIT_UPDATE      int64  
 16  AMT_ANNUITY             float64
dtypes: float64(8), int64(6), object(3)
memory usage: 222.6+ MB
None

bureau_balance: shape is (27299925, 3)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27299925 entries, 0 to 27299924
Data columns (total 3 columns):
 #   Column          Dtype 
---  ------          ----- 
 0   SK_ID_BUREAU    int64 
 1   MONTHS_BALANCE  int64 
 2   STATUS          object
dtypes: int64(2), object(1)
memory usage: 624.8+ MB
None

credit_card_balance: shape is (3840312, 23)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3840312 entries, 0 to 3840311
Data columns (total 23 columns):
 #   Column                      Dtype  
---  ------                      -----  
 0   SK_ID_PREV                  int64  
 1   SK_ID_CURR                  int64  
 2   MONTHS_BALANCE              int64  
 3   AMT_BALANCE                 float64
 4   AMT_CREDIT_LIMIT_ACTUAL     int64  
 5   AMT_DRAWINGS_ATM_CURRENT    float64
 6   AMT_DRAWINGS_CURRENT        float64
 7   AMT_DRAWINGS_OTHER_CURRENT  float64
 8   AMT_DRAWINGS_POS_CURRENT    float64
 9   AMT_INST_MIN_REGULARITY     float64
 10  AMT_PAYMENT_CURRENT         float64
 11  AMT_PAYMENT_TOTAL_CURRENT   float64
 12  AMT_RECEIVABLE_PRINCIPAL    float64
 13  AMT_RECIVABLE               float64
 14  AMT_TOTAL_RECEIVABLE        float64
 15  CNT_DRAWINGS_ATM_CURRENT    float64
 16  CNT_DRAWINGS_CURRENT        int64  
 17  CNT_DRAWINGS_OTHER_CURRENT  float64
 18  CNT_DRAWINGS_POS_CURRENT    float64
 19  CNT_INSTALMENT_MATURE_CUM   float64
 20  NAME_CONTRACT_STATUS        object 
 21  SK_DPD                      int64  
 22  SK_DPD_DEF                  int64  
dtypes: float64(15), int64(7), object(1)
memory usage: 673.9+ MB
None

installments_payments: shape is (13605401, 8)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13605401 entries, 0 to 13605400
Data columns (total 8 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_PREV              int64  
 1   SK_ID_CURR              int64  
 2   NUM_INSTALMENT_VERSION  float64
 3   NUM_INSTALMENT_NUMBER   int64  
 4   DAYS_INSTALMENT         float64
 5   DAYS_ENTRY_PAYMENT      float64
 6   AMT_INSTALMENT          float64
 7   AMT_PAYMENT             float64
dtypes: float64(5), int64(3)
memory usage: 830.4 MB
None

previous_application: shape is (1670214, 37)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB
None

POS_CASH_balance: shape is (10001358, 8)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10001358 entries, 0 to 10001357
Data columns (total 8 columns):
 #   Column                 Dtype  
---  ------                 -----  
 0   SK_ID_PREV             int64  
 1   SK_ID_CURR             int64  
 2   MONTHS_BALANCE         int64  
 3   CNT_INSTALMENT         float64
 4   CNT_INSTALMENT_FUTURE  float64
 5   NAME_CONTRACT_STATUS   object 
 6   SK_DPD                 int64  
 7   SK_DPD_DEF             int64  
dtypes: float64(2), int64(5), object(1)
memory usage: 610.4+ MB
None

CPU times: user 15.6 s, sys: 2.61 s, total: 18.2 s
Wall time: 18.4 s


for ds_name in datasets.keys():
    print(f'dataset {ds_name:24}: [ {datasets[ds_name].shape[0]:10,}, {datasets[ds_name].shape[1]}]')

dataset application_train       : [    307,511, 122]
dataset application_test        : [     48,744, 121]
dataset bureau                  : [  1,716,428, 17]
dataset bureau_balance          : [ 27,299,925, 3]
dataset credit_card_balance     : [  3,840,312, 23]
dataset installments_payments   : [ 13,605,401, 8]
dataset previous_application    : [  1,670,214, 37]
dataset POS_CASH_balance        : [ 10,001,358, 8]


# Access the 'application_train' dataset from the 'datasets' container
application_train = datasets['application_train']

# Select the minority class instances (TARGET = 1) from the training dataset
minority_application_train = application_train[application_train['TARGET']==1]

# Append a randomly sampled subset of majority class instances (TARGET = 0) to the minority class instances
undersampled_application_train = minority_application_train.append(
    application_train[application_train['TARGET']==0].reset_index(drop=True).sample(n = 75000)
)


# Assign the undersampled training dataset to a new key in the 'datasets' dictionary
datasets["undersampled_application_train"] = undersampled_application_train 

# Count the number of instances in each class
class_distribution = undersampled_application_train['TARGET'].value_counts()

# Print the class distribution
print("Class distribution in the undersampled training dataset:")
print(class_distribution)

Class distribution in the undersampled training dataset:
0    75000
1    24825
Name: TARGET, dtype: int64


  # Assuming this is a dictionary where you store your datasets

# Filtering rows with TARGET == 1 and creating a new DataFrame
datasets["undersampled_application_train_2"] = datasets["application_train"][datasets["application_train"].TARGET == 1].copy()
datasets["undersampled_application_train_2"]['weight'] = 1

# Undersampling Cash loans
num_default_cashloans = len(datasets["undersampled_application_train_2"][(datasets["undersampled_application_train_2"].NAME_CONTRACT_TYPE == 'Cash loans') & (datasets["undersampled_application_train_2"].TARGET == 1)])
df_sample_cash = datasets["application_train"][(datasets["application_train"].NAME_CONTRACT_TYPE == 'Cash loans') & (datasets["application_train"].TARGET == 0)].sample(n=num_default_cashloans, random_state=42)
df_sample_cash['weight'] = 1

# Undersampling Revolving loans
num_default_revolvingloans = len(datasets["undersampled_application_train_2"][(datasets["undersampled_application_train_2"].NAME_CONTRACT_TYPE == 'Revolving loans') & (datasets["undersampled_application_train_2"].TARGET == 1)])
df_sample_revolving = datasets["application_train"][(datasets["application_train"].NAME_CONTRACT_TYPE == 'Revolving loans') & (datasets["application_train"].TARGET == 0)].sample(n=num_default_revolvingloans, random_state=42)
df_sample_revolving['weight'] = 1

# Combining undersampled cash loans and revolving loans with the initial DataFrame
datasets["undersampled_application_train_2"] = pd.concat([datasets["undersampled_application_train_2"], df_sample_cash, df_sample_revolving])

# Check the distribution of the TARGET variable
print(datasets["undersampled_application_train_2"].TARGET.value_counts())

1    24825
0    24825
Name: TARGET, dtype: int64


# Assuming this is a dictionary where you store your datasets

# Filtering rows with TARGET == 1 and creating a new DataFrame
undersampled_application_train_2 = datasets["application_train"][datasets["application_train"].TARGET == 1].copy()
undersampled_application_train_2['weight'] = 1

# Undersampling Cash loans
num_default_cashloans = len(undersampled_application_train_2[(undersampled_application_train_2.NAME_CONTRACT_TYPE == 'Cash loans') & (undersampled_application_train_2.TARGET == 1)])
df_sample_cash = datasets["application_train"][(datasets["application_train"].NAME_CONTRACT_TYPE == 'Cash loans') & (datasets["application_train"].TARGET == 0)].sample(n=num_default_cashloans, random_state=42)
df_sample_cash['weight'] = 1

# Undersampling Revolving loans
num_default_revolvingloans = len(undersampled_application_train_2[(undersampled_application_train_2.NAME_CONTRACT_TYPE == 'Revolving loans') & (undersampled_application_train_2.TARGET == 1)])
df_sample_revolving = datasets["application_train"][(datasets["application_train"].NAME_CONTRACT_TYPE == 'Revolving loans') & (datasets["application_train"].TARGET == 0)].sample(n=num_default_revolvingloans, random_state=42)
df_sample_revolving['weight'] = 1

# Combining undersampled cash loans and revolving loans with the initial DataFrame
undersampled_application_train_2 = pd.concat([undersampled_application_train_2, df_sample_cash, df_sample_revolving])

# Check the distribution of the TARGET variable
print(undersampled_application_train_2.TARGET.value_counts())

1    24825
0    24825
Name: TARGET, dtype: int64


# Create aggregate features (via pipeline)
class FeaturesAggregater(BaseEstimator, TransformerMixin):

    def __init__(self, features=None, agg_needed=["mean"]): # no *args or **kargs self.features = features
        self.agg_needed = agg_needed
        self.agg_op_features = {}
        for f in features:
            self.agg_op_features[f] = self.agg_needed[:]
    def fit(self, X, y=None): 
        return self
    def transform(self, X, y=None):
        result = X.groupby(["SK_ID_CURR"]).agg(self.agg_op_features) 
        df_result = pd.DataFrame()
        for x1, x2 in result.columns:
            new_col = x1 + "_" + x2
            df_result[new_col] = result[x1][x2]
            df_result = df_result.reset_index(level=["SK_ID_CURR"]) 
            return df_result


# Access the 'previous_application' dataset from the 'datasets' container and assign it to a variable named 'previous_application_data'
previous_application_data = datasets["previous_application"]

# Apply the 'isna()' method on the 'previous_application_data' DataFrame to detect missing or null values, 
# and then apply the 'sum()' method to count the number of missing values in each column of the DataFrame.
missing_values_count_per_column = previous_application_data.isna().sum()
missing_values_count_per_column

SK_ID_PREV                           0
SK_ID_CURR                           0
NAME_CONTRACT_TYPE                   0
AMT_ANNUITY                     372235
AMT_APPLICATION                      0
AMT_CREDIT                           1
AMT_DOWN_PAYMENT                895844
AMT_GOODS_PRICE                 385515
WEEKDAY_APPR_PROCESS_START           0
HOUR_APPR_PROCESS_START              0
FLAG_LAST_APPL_PER_CONTRACT          0
NFLAG_LAST_APPL_IN_DAY               0
RATE_DOWN_PAYMENT               895844
RATE_INTEREST_PRIMARY          1664263
RATE_INTEREST_PRIVILEGED       1664263
NAME_CASH_LOAN_PURPOSE               0
NAME_CONTRACT_STATUS                 0
DAYS_DECISION                        0
NAME_PAYMENT_TYPE                    0
CODE_REJECT_REASON                   0
NAME_TYPE_SUITE                 820405
NAME_CLIENT_TYPE                     0
NAME_GOODS_CATEGORY                  0
NAME_PORTFOLIO                       0
NAME_PRODUCT_TYPE                    0
CHANNEL_TYPE                         0
SELLERPLACE_AREA                     0
NAME_SELLER_INDUSTRY                 0
CNT_PAYMENT                     372230
NAME_YIELD_GROUP                     0
PRODUCT_COMBINATION                346
DAYS_FIRST_DRAWING              673065
DAYS_FIRST_DUE                  673065
DAYS_LAST_DUE_1ST_VERSION       673065
DAYS_LAST_DUE                   673065
DAYS_TERMINATION                673065
NFLAG_INSURED_ON_APPROVAL       673065
dtype: int64


previous_feature = ["AMT_APPLICATION", "AMT_CREDIT", "AMT_ANNUITY", "approved_credit_ratio", "AMT_ANNUITY_credit_ratio", "Interest_ratio", "LTV_ratio", "SK_ID_PREV", "approved"]
agg_needed = ["min", "max", "mean", "count", "sum"]


agg_needed = ["min", "max", "mean", "count", "sum"]

def previous_feature_aggregation(df, feature, agg_needed):
    df['approved_credit_ratio'] = (df['AMT_APPLICATION']/df['AMT_CREDIT']).replace(np.inf, 0)
    # installment over credit approved ratio
    df['AMT_ANNUITY_credit_ratio'] = (df['AMT_ANNUITY']/df['AMT_CREDIT']).replace(np.inf, 0)
    # total interest payment over credit ratio
    df['Interest_ratio'] = (df['AMT_ANNUITY']/df['AMT_CREDIT']).replace(np.inf, 0)
    #  loan cover ratio
    df['LTV_ratio'] = (df['AMT_CREDIT']/df['AMT_GOODS_PRICE']).replace(np.inf, 0)
    df['approved'] = np.where(df.AMT_CREDIT >0 ,1, 0)
    
    test_pipeline = make_pipeline(FeaturesAggregater(feature, agg_needed))
    return(test_pipeline.fit_transform(df))
    
datasets['previous_application_agg'] = previous_feature_aggregation(datasets["previous_application"], previous_feature, agg_needed)


datasets["previous_application_agg"].isna().sum()

SK_ID_CURR             0
AMT_APPLICATION_min    0
dtype: int64


datasets["installments_payments"].isna().sum()

SK_ID_PREV                   0
SK_ID_CURR                   0
NUM_INSTALMENT_VERSION       0
NUM_INSTALMENT_NUMBER        0
DAYS_INSTALMENT              0
DAYS_ENTRY_PAYMENT        2905
AMT_INSTALMENT               0
AMT_PAYMENT               2905
dtype: int64


payments_features = ["DAYS_INSTALMENT_DIFF", "AMT_PATMENT_PCT"]

agg_needed = ["mean"]

def payments_feature_aggregation(df, feature, agg_needed):
    df['DAYS_INSTALMENT_DIFF'] = df['DAYS_INSTALMENT'] - df['DAYS_ENTRY_PAYMENT']
    df['AMT_PATMENT_PCT'] = [x/y if (y != 0) & pd.notnull(y) else np.nan for x,y in zip(df.AMT_PAYMENT,df.AMT_INSTALMENT)]
    
    test_pipeline = make_pipeline(FeaturesAggregater(feature, agg_needed))
    return(test_pipeline.fit_transform(df))
    
datasets['installments_payments_agg'] = payments_feature_aggregation(datasets["installments_payments"], payments_features, agg_needed)


datasets["installments_payments_agg"].isna().sum()

SK_ID_CURR                   0
DAYS_INSTALMENT_DIFF_mean    9
dtype: int64


datasets["credit_card_balance"].isna().sum()

SK_ID_PREV                         0
SK_ID_CURR                         0
MONTHS_BALANCE                     0
AMT_BALANCE                        0
AMT_CREDIT_LIMIT_ACTUAL            0
AMT_DRAWINGS_ATM_CURRENT      749816
AMT_DRAWINGS_CURRENT               0
AMT_DRAWINGS_OTHER_CURRENT    749816
AMT_DRAWINGS_POS_CURRENT      749816
AMT_INST_MIN_REGULARITY       305236
AMT_PAYMENT_CURRENT           767988
AMT_PAYMENT_TOTAL_CURRENT          0
AMT_RECEIVABLE_PRINCIPAL           0
AMT_RECIVABLE                      0
AMT_TOTAL_RECEIVABLE               0
CNT_DRAWINGS_ATM_CURRENT      749816
CNT_DRAWINGS_CURRENT               0
CNT_DRAWINGS_OTHER_CURRENT    749816
CNT_DRAWINGS_POS_CURRENT      749816
CNT_INSTALMENT_MATURE_CUM     305236
NAME_CONTRACT_STATUS               0
SK_DPD                             0
SK_DPD_DEF                         0
dtype: int64


credit_features = [
    "AMT_BALANCE",
    "AMT_DRAWINGS_PCT",
    "AMT_DRAWINGS_ATM_PCT",
    "AMT_DRAWINGS_OTHER_PCT",
    "AMT_DRAWINGS_POS_PCT",
    "AMT_PRINCIPAL_RECEIVABLE_PCT",
    "CNT_DRAWINGS_ATM_CURRENT",
    "CNT_DRAWINGS_CURRENT",
    "CNT_DRAWINGS_OTHER_CURRENT",
    "CNT_DRAWINGS_POS_CURRENT",
    "SK_DPD",
    "SK_DPD_DEF",
]

agg_needed = ["mean"]


def calculate_pct(x, y):
    return x / y if (y != 0) & pd.notnull(y) else np.nan
#def pct(x, y):
    #return x / y if (y != 0) & pd.notnull(y) else np.nan


def credit_feature_aggregation(df, feature, agg_needed):
    pct_columns = [
        ("AMT_DRAWINGS_CURRENT", "AMT_DRAWINGS_PCT"),
        ("AMT_DRAWINGS_ATM_CURRENT", "AMT_DRAWINGS_ATM_PCT"),
        ("AMT_DRAWINGS_OTHER_CURRENT", "AMT_DRAWINGS_OTHER_PCT"),
        ("AMT_DRAWINGS_POS_CURRENT", "AMT_DRAWINGS_POS_PCT"),
        ("AMT_RECEIVABLE_PRINCIPAL", "AMT_PRINCIPAL_RECEIVABLE_PCT"),
    ]

    for col_x, col_pct in pct_columns:
        df[col_pct] = [calculate_pct(x, y) for x, y in zip(df[col_x], df["AMT_CREDIT_LIMIT_ACTUAL"])]

    pipeline = make_pipeline(FeaturesAggregater(feature, agg_needed))
    return pipeline.fit_transform(df)


datasets["credit_card_balance_agg"] = credit_feature_aggregation(
    datasets["credit_card_balance"], credit_features, agg_needed
)


datasets["credit_card_balance_agg"].isna().sum()

SK_ID_CURR          0
AMT_BALANCE_mean    0
dtype: int64


datasets.keys()

dict_keys(['application_train', 'application_test', 'bureau', 'bureau_balance', 'credit_card_balance', 'installments_payments', 'previous_application', 'POS_CASH_balance', 'undersampled_application_train', 'undersampled_application_train_2', 'previous_application_agg', 'installments_payments_agg', 'credit_card_balance_agg'])


# Load the train dataset
train_data = datasets["application_train"]

# Compute the distribution of the target variable
target_counts = train_data['TARGET'].value_counts()

# Display the target distribution
print("Target variable distribution:\n")
print(target_counts)
print("\n")

# Compute the percentage of positive and negative examples in the dataset
positive_count = target_counts[1]
negative_count = target_counts[0]
total_count = positive_count + negative_count
positive_percentage = (positive_count / total_count) * 100
negative_percentage = (negative_count / total_count) * 100

# Display the percentages of positive and negative examples
print(f"Percentage of positive examples: {positive_percentage:.2f}%")
print(f"Percentage of negative examples: {negative_percentage:.2f}%")

Target variable distribution:

0    282686
1     24825
Name: TARGET, dtype: int64


Percentage of positive examples: 8.07%
Percentage of negative examples: 91.93%


train_dataset= datasets["undersampled_application_train"] #primary dataset
    
merge_all_data = True

# merge primary table and secondary tables using features based on meta data and  aggregage stats 
if merge_all_data:
    # 1. Join/Merge in prevApps Data
    train_dataset = train_dataset.merge(datasets["previous_application_agg"], how='left', on='SK_ID_CURR')

    # 2. Join/Merge in Installments Payments  Data
    train_dataset = train_dataset.merge(datasets["installments_payments_agg"], how='left', on="SK_ID_CURR")

    # 3. Join/Merge in Credit Card Balance Data
    train_dataset = train_dataset.merge(datasets["credit_card_balance_agg"], how='left', on="SK_ID_CURR")


datasets["undersampled_application_train_4"] = train_dataset


train_dataset.shape

(99825, 125)


train_dataset = datasets["undersampled_application_train_2"]
train_dataset = train_dataset.merge(datasets["previous_application_agg"], how='left', on='SK_ID_CURR')
train_dataset = train_dataset.merge(datasets["installments_payments_agg"], how='left', on="SK_ID_CURR")
train_dataset = train_dataset.merge(datasets["credit_card_balance_agg"], how='left', on="SK_ID_CURR")
train_dataset = train_dataset.drop(columns = 'weight')
datasets["undersampled_application_train_4_2"] = train_dataset


train_dataset.shape

(49650, 125)


train_dataset.to_csv('train_dataset.csv', index=False)


X_kaggle_test= datasets["application_test"]

# merge primary table and secondary tables using features based on meta data and  aggregage stats 
if merge_all_data:
    # 1. Join/Merge in prevApps Data
    X_kaggle_test = X_kaggle_test.merge(datasets["previous_application_agg"], how='left', on='SK_ID_CURR')

    # 2. Join/Merge in Installments Payments  Data
    X_kaggle_test = X_kaggle_test.merge(datasets["installments_payments_agg"], how='left', on="SK_ID_CURR")

    # 3. Join/Merge in Credit Card Balance Data
    X_kaggle_test = X_kaggle_test.merge(datasets["credit_card_balance_agg"], how='left', on="SK_ID_CURR")


X_kaggle_test.shape

(48744, 124)


X_kaggle_test.to_csv('X_kaggle_test.csv', index=False)


train_dataset = pd.read_csv("train_dataset.csv")
train_dataset.head()


train_dataset.shape

(49650, 125)


# import pandas as pd
# import pandas_profiling

# # Create the report
# train_dataset_profile = pandas_profiling.ProfileReport(train_dataset)
# train_dataset_profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]


#train_dataset = pd.read_csv("train_dataset.csv")
X_kaggle_test = pd.read_csv("X_kaggle_test.csv")
X_kaggle_test.head()


X_kaggle_test.shape

(48744, 124)


# class to select numerical or categorical columns
class DataFrameCreation(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values

def pct(x):
    return round(100*x,3)

def get_pipeline(dataset, num_cols = None):

    numerical_features = []
    categorical_features = []
    for x in dataset:
        if(dataset[x].dtype == np.float64 or dataset[x].dtype == np.int64):
            numerical_features.append(x)
        else:
            categorical_features.append(x)
    numerical_features.remove('TARGET')
    numerical_features.remove('SK_ID_CURR')

    categorical_pipeline = Pipeline([
            ('selector', DataFrameCreation(categorical_features)),
            ('imputer', SimpleImputer(strategy='most_frequent')),
            ('ohe', OneHotEncoder(sparse=False, handle_unknown="ignore"))
        ])
    
    # If columns are provided, we use only pass those columns to the model
    if num_cols == None:
        final_numerical_features = numerical_features
    else:
        final_numerical_features = num_cols
        
    numerical_pipeline = Pipeline([
            ('selector', DataFrameCreation(final_numerical_features)),
            ('imputer', SimpleImputer(strategy='mean')),
            ('std_scaler', StandardScaler()),
        ])

    data_pipeline = FeatureUnion(transformer_list=[
            ("numerical_pipeline", numerical_pipeline),
            ("categorical_pipeline", categorical_pipeline),
        ])  

    selected_features = final_numerical_features + categorical_features + ["SK_ID_CURR"]
    tot_features = f"{len(selected_features)}:   Num:{len(final_numerical_features)},    Cat:{len(categorical_features)}"

    print('Total Features:', tot_features)
    
    return data_pipeline, selected_features


data_pipeline, selected_features = get_pipeline(train_dataset)

Total Features: 124:   Num:107,    Cat:16


y_train = train_dataset['TARGET']
X_train = train_dataset[selected_features]
X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2, random_state=42)

print(f"X train           shape: {X_train.shape}")
print(f"X test            shape: {X_test.shape}")

X train           shape: (39720, 124)
X test            shape: (9930, 124)


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)

cpu


# Handling missing values and standardizing the data
X_train_std = data_pipeline.fit_transform(X_train)
X_test_std = data_pipeline.transform(X_test)
X_kaggle_test_std = data_pipeline.transform(X_kaggle_test)

# Converting numpy arrays into float tensors using gpu device
X_train_tensor = torch.FloatTensor(X_train_std).to(device)
X_test_tensor = torch.FloatTensor(X_test_std).to(device)
X_kaggle_test_tensor = torch.FloatTensor(X_kaggle_test_std).to(device)

# Converting numpy arrays to float tensors and reshaping y_train and y_test
y_train_tensor = torch.FloatTensor(y_train.to_numpy()).to(device)
y_train_tensor = y_train_tensor.reshape(-1, 1)
y_test_tensor = torch.FloatTensor(y_test.to_numpy()).to(device)
y_test_tensor = y_test_tensor.reshape(-1, 1)


X_train_tensor.shape, X_test_tensor.shape, X_kaggle_test_tensor.shape

(torch.Size([39720, 245]), torch.Size([9930, 245]), torch.Size([48744, 245]))


# Loading features and importances from phase3
with open("features_dict_XG.pickle", 'rb') as handle:
    features_dict = pickle.load(handle)

# selecting features with importance values > 0
features = features_dict['features']
importances = features_dict['importances']
new_indices = [idx for idx, x in enumerate(importances) if x > 0]
new_importances = [x for idx, x in enumerate(importances) if x > 0]
new_features = [features[i] for i in new_indices]

# creating pipeline by joining numerical and categorical pipelines
num_attribs = new_features
data_pipeline, selected_features = get_pipeline(train_dataset, num_attribs)

# splitting the dataset into train and test datasets with selected features
y_train_sel, X_train_sel = train_dataset['TARGET'], train_dataset[selected_features]
X_kaggle_test_sel = X_kaggle_test[selected_features]
X_train_sel, X_test_sel, y_train_sel, y_test_sel = train_test_split(X_train_sel, y_train_sel, test_size=0.2, random_state=42)

# Handling missing values and standardizing the data using pipeline
X_train_sel_std, X_test_sel_std, X_kaggle_test_sel_std = data_pipeline.fit_transform(X_train_sel), data_pipeline.transform(X_test_sel), data_pipeline.transform(X_kaggle_test_sel)

# Generating float tensors from numpy arrays using GPU device
X_train_sel_tensor, X_test_sel_tensor, X_kaggle_test_sel_tensor = map(lambda x: torch.FloatTensor(x).to(device), (X_train_sel_std, X_test_sel_std, X_kaggle_test_sel_std))
y_train_sel_tensor, y_test_sel_tensor = map(lambda x: torch.FloatTensor(x.to_numpy()).reshape(-1, 1).to(device), (y_train_sel, y_test_sel))

# Print the shapes of tensors
print(f"X train selected shape: {X_train_sel_tensor.shape}")
print(f"X test selected shape: {X_test_sel_tensor.shape}")

Total Features: 112:   Num:95,    Cat:16
X train selected shape: torch.Size([39720, 233])
X test selected shape: torch.Size([9930, 233])


%matplotlib inline
writer = SummaryWriter()

from sklearn.metrics import roc_auc_score
>>> y_true = np.array([0, 0, 1, 1])
>>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> roc_auc_score(y_true, y_scores)
0.75


try:
    expLog
except NameError:
    expLog = pd.DataFrame(columns=["exp_name", "learning_rate", "epochs", 
                                   "Train Time (sec)",
                                   "Test Time (sec)", 
                                   "Train Acc", 
                                   "Test Acc",
                                   "Train AUC", 
                                   "Test AUC",
                                   "Train F1", 
                                   "Test F1"
                                  ])


from sklearn.metrics import f1_score

def get_results(expLog, exp_name, learning_rate, epochs, model, train_time, test_time, X_train, y_train, X_test, y_test):
    
    def test_metrics(X, y, model):
      X = X.to(device) # Move the input tensor to the GPU
      model.eval()
      with torch.no_grad():
          y_prob = model(X)
          y_pred = y_prob.cpu().detach().numpy().round()
          roc_auc = roc_auc_score(y, y_pred)
          accuracy = accuracy_score(y, y_pred)
          f1 = f1_score(y, y_pred)
      return accuracy, roc_auc, f1


    # Getting the results
    accuracy_train, roc_auc_train, f1_train = test_metrics(X_train, y_train, model)
    accuracy_test, roc_auc_test, f1_test = test_metrics(X_test, y_test, model)

    expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
               [learning_rate, epochs, train_time, test_time, 
                accuracy_train, accuracy_test, roc_auc_train, roc_auc_test, f1_train, f1_test],
    4))
    return expLog


from sklearn.metrics import f1_score

def train_and_test(X_train_tensor, y_train_tensor, X_test_tensor, y_test_tensor, model, optimizer, writer, learning_rate=0.01, epochs=1000, device='cuda'):
    # Move tensors to the GPU
    X_train_tensor = X_train_tensor.to(device)
    y_train_tensor = y_train_tensor.to(device)
    X_test_tensor = X_test_tensor.to(device)

    # Model to be trained on GPU
    model = model.to(device)

    print('Model Architecture:')
    print(model, '\n')

    print('Training the model:')
    model.train()

    for epoch_id in range(epochs):
        y_prob = model(X_train_tensor)
        loss = binary_cross_entropy(y_prob, y_train_tensor)
        writer.add_scalar("Train Loss", loss, epoch_id+1)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if epoch_id % 50 == 49:
            print(f"Epoch {epoch_id + 1}:")
            show_metrics(y_train_tensor, y_prob, epoch_id+1, writer)

    writer.flush()
    writer.close()
    print()

    # Testing the model
    model.eval()
    with torch.no_grad():
        y_test_pred_prob = model(X_test_tensor)
        y_test_tensor = y_test_tensor.to(device)
        print('Test data:')
        show_metrics(y_test_tensor, y_test_pred_prob, writer=None)

def show_metrics(y_true, y_prob, idx=0, writer=None):
    y_pred = y_prob.cpu().detach().numpy().round()

    # Move tensors to the CPU
    y_true = y_true.cpu()

    # Calculating metrics from actual and predicted values
    roc_auc = roc_auc_score(y_true, y_pred)
    accuracy = accuracy_score(y_true, y_pred)
    f1 = f1_score(y_true, y_pred)

    if writer:
        # Adding info to tensorboard
        writer.add_scalar("Train ROC_AUC", roc_auc, idx)
        writer.add_scalar("Train Accuracy", accuracy, idx)
        writer.add_scalar("Train F1", f1, idx)

    # Printing accuracy, ROC_AUC, and F1 for reference
    print(f'Accuracy : {round(accuracy,4)} ; ROC_AUC : {round(roc_auc, 4)} ; F1 : {round(f1, 4)}')


def train_and_test(X_train_tensor, y_train_tensor, X_test_tensor, y_test_tensor, model, optimizer, writer, learning_rate=0.01, epochs=1000, device='cuda'):
    # Move tensors to the GPU
    X_train_tensor = X_train_tensor.to(device)
    y_train_tensor = y_train_tensor.to(device)
    X_test_tensor = X_test_tensor.to(device)
    y_test_tensor = y_test_tensor.to(device)

    # Model to be trained on GPU
    model = model.to(device)

    print('Model Architecture:')
    print(model, '\n')

    print('Training the model:')
    model.train()

    for epoch_id in range(epochs):
        y_prob = model(X_train_tensor)
        loss = binary_cross_entropy(y_prob, y_train_tensor)
        writer.add_scalar("Train Loss", loss, epoch_id+1)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if epoch_id % 50 == 49:
            print(f"Epoch {epoch_id + 1}:")
            show_metrics(y_train_tensor, y_prob, epoch_id+1, writer)

    writer.flush()
    writer.close()
    print()

    # Testing the model
    model.eval()
    with torch.no_grad():
        y_test_pred_prob = model(X_test_tensor)
        print('Test data:')
        show_metrics(y_test_tensor, y_test_pred_prob, writer=None)

def show_metrics(y_true, y_prob, idx=0, writer=None):
    y_pred = y_prob.cpu().detach().numpy().round()

    # Move tensors to the CPU
    y_true = y_true.cpu()

    # Calculating metrics from actual and predicted values
    roc_auc = roc_auc_score(y_true.cpu().numpy(), y_pred)
    accuracy = accuracy_score(y_true.cpu().numpy(), y_pred)
    f1 = f1_score(y_true.cpu().numpy(), y_pred)

    if writer:
        # Adding info to tensorboard
        writer.add_scalar("Train ROC_AUC", roc_auc, idx)
        writer.add_scalar("Train Accuracy", accuracy, idx)
        writer.add_scalar("Train F1", f1, idx)

    # Printing accuracy, ROC_AUC, and F1 for reference
    print(f'Accuracy : {round(accuracy,4)} ; ROC_AUC : {round(roc_auc, 4)} ; F1 : {round(f1, 4)}')


from IPython.display import Image
Image(filename='p4block.jpeg')


import torch
import torch.nn as nn

# Define input and output dimensions
dim_input = X_train_tensor.shape[1]
dim_output = 1

# Define the model architecture
model1 = torch.nn.Sequential(
    torch.nn.Linear(dim_input, dim_output),
    nn.Sigmoid()
)


from torchsummary import summary

# Print summary of model architecture
summary(model1, input_size=(X_train_tensor.shape[1],), device='cpu')

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Linear-1                    [-1, 1]             246
           Sigmoid-2                    [-1, 1]               0
================================================================
Total params: 246
Trainable params: 246
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.00
Params size (MB): 0.00
Estimated Total Size (MB): 0.00
----------------------------------------------------------------


import time
import numpy as np
from torch.optim import Adam

model = model1
learning_rate = 0.01
epochs = 1000
optimizer = Adam(model.parameters(), learning_rate)
y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)
y_test=y_test_tensor
# Training the model
start_time = time.time()
train_and_test(X_train_tensor, y_train_tensor, X_test_tensor, y_test, model, optimizer, writer, learning_rate, epochs)
train_time = np.round(time.time() - start_time, 4)

# Testing the model
start_time = time.time()
train_and_test(X_train_tensor, y_train_tensor, X_test_tensor, y_test, model, optimizer, writer, learning_rate, epochs)
test_time = np.round(time.time() - start_time, 4)

print(f'Training time: {train_time} seconds')
print(f'Testing time: {test_time} seconds')

Model Architecture:
Sequential(
  (0): Linear(in_features=245, out_features=1, bias=True)
  (1): Sigmoid()
) 

Training the model:
Epoch 50:
Accuracy : 0.687 ; ROC_AUC : 0.687 ; F1 : 0.6863
Epoch 100:
Accuracy : 0.6886 ; ROC_AUC : 0.6886 ; F1 : 0.6878
Epoch 150:
Accuracy : 0.6887 ; ROC_AUC : 0.6887 ; F1 : 0.6878
Epoch 200:
Accuracy : 0.6894 ; ROC_AUC : 0.6894 ; F1 : 0.6884
Epoch 250:
Accuracy : 0.6897 ; ROC_AUC : 0.6897 ; F1 : 0.6888
Epoch 300:
Accuracy : 0.6899 ; ROC_AUC : 0.6899 ; F1 : 0.689
Epoch 350:
Accuracy : 0.69 ; ROC_AUC : 0.69 ; F1 : 0.6891
Epoch 400:
Accuracy : 0.6898 ; ROC_AUC : 0.6898 ; F1 : 0.6889
Epoch 450:
Accuracy : 0.6901 ; ROC_AUC : 0.6901 ; F1 : 0.6893
Epoch 500:
Accuracy : 0.6899 ; ROC_AUC : 0.6899 ; F1 : 0.6891
Epoch 550:
Accuracy : 0.6901 ; ROC_AUC : 0.6901 ; F1 : 0.6893
Epoch 600:
Accuracy : 0.6902 ; ROC_AUC : 0.6902 ; F1 : 0.6894
Epoch 650:
Accuracy : 0.6905 ; ROC_AUC : 0.6905 ; F1 : 0.6897
Epoch 700:
Accuracy : 0.6906 ; ROC_AUC : 0.6906 ; F1 : 0.6898
Epoch 750:
Accuracy : 0.6906 ; ROC_AUC : 0.6906 ; F1 : 0.6897
Epoch 800:
Accuracy : 0.6907 ; ROC_AUC : 0.6907 ; F1 : 0.6898
Epoch 850:
Accuracy : 0.6907 ; ROC_AUC : 0.6907 ; F1 : 0.6898
Epoch 900:
Accuracy : 0.6906 ; ROC_AUC : 0.6906 ; F1 : 0.6898
Epoch 950:
Accuracy : 0.6907 ; ROC_AUC : 0.6907 ; F1 : 0.6899
Epoch 1000:
Accuracy : 0.6908 ; ROC_AUC : 0.6908 ; F1 : 0.69

Test data:
Accuracy : 0.6813 ; ROC_AUC : 0.6813 ; F1 : 0.6814
Model Architecture:
Sequential(
  (0): Linear(in_features=245, out_features=1, bias=True)
  (1): Sigmoid()
) 

Training the model:
Epoch 50:
Accuracy : 0.6908 ; ROC_AUC : 0.6908 ; F1 : 0.69
Epoch 100:
Accuracy : 0.6908 ; ROC_AUC : 0.6908 ; F1 : 0.69
Epoch 150:
Accuracy : 0.6909 ; ROC_AUC : 0.6909 ; F1 : 0.6901
Epoch 200:
Accuracy : 0.6909 ; ROC_AUC : 0.6909 ; F1 : 0.6902
Epoch 250:
Accuracy : 0.691 ; ROC_AUC : 0.691 ; F1 : 0.6902
Epoch 300:
Accuracy : 0.6909 ; ROC_AUC : 0.6909 ; F1 : 0.6902
Epoch 350:
Accuracy : 0.691 ; ROC_AUC : 0.691 ; F1 : 0.6904
Epoch 400:
Accuracy : 0.691 ; ROC_AUC : 0.691 ; F1 : 0.6903
Epoch 450:
Accuracy : 0.6912 ; ROC_AUC : 0.6912 ; F1 : 0.6905
Epoch 500:
Accuracy : 0.6911 ; ROC_AUC : 0.6911 ; F1 : 0.6905
Epoch 550:
Accuracy : 0.6911 ; ROC_AUC : 0.6911 ; F1 : 0.6905
Epoch 600:
Accuracy : 0.691 ; ROC_AUC : 0.691 ; F1 : 0.6904
Epoch 650:
Accuracy : 0.6902 ; ROC_AUC : 0.6902 ; F1 : 0.6888
Epoch 700:
Accuracy : 0.6912 ; ROC_AUC : 0.6912 ; F1 : 0.6907
Epoch 750:
Accuracy : 0.6911 ; ROC_AUC : 0.6911 ; F1 : 0.6905
Epoch 800:
Accuracy : 0.691 ; ROC_AUC : 0.691 ; F1 : 0.6904
Epoch 850:
Accuracy : 0.6908 ; ROC_AUC : 0.6908 ; F1 : 0.6902
Epoch 900:
Accuracy : 0.6901 ; ROC_AUC : 0.6901 ; F1 : 0.6882
Epoch 950:
Accuracy : 0.6909 ; ROC_AUC : 0.6909 ; F1 : 0.6904
Epoch 1000:
Accuracy : 0.691 ; ROC_AUC : 0.691 ; F1 : 0.6904

Test data:
Accuracy : 0.6828 ; ROC_AUC : 0.6828 ; F1 : 0.6832
Training time: 5.0025 seconds
Testing time: 3.6912 seconds


exp_name = f"Model1 All"
expLog = get_results(expLog, exp_name, learning_rate, epochs, model, train_time, test_time, X_train_tensor, y_train, X_test_tensor, y_test)
expLog


%load_ext tensorboard


tensorboard --logdir=runs


dim_input = X_train_sel_tensor.shape[1]
dim_output = 1
model1 = torch.nn.Sequential( 
    torch.nn.Linear(dim_input, dim_output),
    nn.Sigmoid())


model = model1
learning_rate = 0.01
epochs = 1000
optimizer = Adam(model.parameters(), learning_rate)

y_test_tensor = torch.tensor(y_test_sel.values, dtype=torch.float32)
y_test_sel=y_test_tensor

# Training the model
start_time = time.time()
train_and_test(X_train_sel_tensor, y_train_sel_tensor, X_test_sel_tensor, y_test_sel, model, optimizer, writer, learning_rate, epochs)
train_time = np.round(time.time() - start_time, 4)

# Testing the model
start_time = time.time()
train_and_test(X_train_sel_tensor, y_train_sel_tensor, X_test_sel_tensor, y_test_sel, model, optimizer, writer, learning_rate, epochs)
test_time = np.round(time.time() - start_time, 4)

print(f'Training time: {train_time} seconds')
print(f'Testing time: {test_time} seconds')

Model Architecture:
Sequential(
  (0): Linear(in_features=233, out_features=1, bias=True)
  (1): Sigmoid()
) 

Training the model:
Epoch 50:
Accuracy : 0.6873 ; ROC_AUC : 0.6873 ; F1 : 0.6872
Epoch 100:
Accuracy : 0.6883 ; ROC_AUC : 0.6883 ; F1 : 0.6875
Epoch 150:
Accuracy : 0.6885 ; ROC_AUC : 0.6885 ; F1 : 0.6876
Epoch 200:
Accuracy : 0.6895 ; ROC_AUC : 0.6895 ; F1 : 0.6886
Epoch 250:
Accuracy : 0.6897 ; ROC_AUC : 0.6897 ; F1 : 0.6888
Epoch 300:
Accuracy : 0.6896 ; ROC_AUC : 0.6896 ; F1 : 0.6888
Epoch 350:
Accuracy : 0.6898 ; ROC_AUC : 0.6898 ; F1 : 0.6889
Epoch 400:
Accuracy : 0.6897 ; ROC_AUC : 0.6897 ; F1 : 0.6888
Epoch 450:
Accuracy : 0.6896 ; ROC_AUC : 0.6896 ; F1 : 0.6886
Epoch 500:
Accuracy : 0.6898 ; ROC_AUC : 0.6898 ; F1 : 0.6889
Epoch 550:
Accuracy : 0.6897 ; ROC_AUC : 0.6897 ; F1 : 0.6888
Epoch 600:
Accuracy : 0.6899 ; ROC_AUC : 0.6899 ; F1 : 0.6892
Epoch 650:
Accuracy : 0.6899 ; ROC_AUC : 0.6899 ; F1 : 0.6891
Epoch 700:
Accuracy : 0.6899 ; ROC_AUC : 0.6899 ; F1 : 0.6892
Epoch 750:
Accuracy : 0.69 ; ROC_AUC : 0.69 ; F1 : 0.6892
Epoch 800:
Accuracy : 0.6899 ; ROC_AUC : 0.6899 ; F1 : 0.6891
Epoch 850:
Accuracy : 0.6899 ; ROC_AUC : 0.6899 ; F1 : 0.6891
Epoch 900:
Accuracy : 0.69 ; ROC_AUC : 0.69 ; F1 : 0.6896
Epoch 950:
Accuracy : 0.6897 ; ROC_AUC : 0.6897 ; F1 : 0.6889
Epoch 1000:
Accuracy : 0.69 ; ROC_AUC : 0.69 ; F1 : 0.6892

Test data:
Accuracy : 0.6812 ; ROC_AUC : 0.6812 ; F1 : 0.6813
Model Architecture:
Sequential(
  (0): Linear(in_features=233, out_features=1, bias=True)
  (1): Sigmoid()
) 

Training the model:
Epoch 50:
Accuracy : 0.6899 ; ROC_AUC : 0.6899 ; F1 : 0.6892
Epoch 100:
Accuracy : 0.6899 ; ROC_AUC : 0.6899 ; F1 : 0.6892
Epoch 150:
Accuracy : 0.69 ; ROC_AUC : 0.69 ; F1 : 0.6893
Epoch 200:
Accuracy : 0.69 ; ROC_AUC : 0.69 ; F1 : 0.6896
Epoch 250:
Accuracy : 0.69 ; ROC_AUC : 0.69 ; F1 : 0.6892
Epoch 300:
Accuracy : 0.6899 ; ROC_AUC : 0.6899 ; F1 : 0.6892
Epoch 350:
Accuracy : 0.6899 ; ROC_AUC : 0.6899 ; F1 : 0.6893
Epoch 400:
Accuracy : 0.69 ; ROC_AUC : 0.69 ; F1 : 0.6894
Epoch 450:
Accuracy : 0.6905 ; ROC_AUC : 0.6905 ; F1 : 0.6912
Epoch 500:
Accuracy : 0.6902 ; ROC_AUC : 0.6902 ; F1 : 0.6896
Epoch 550:
Accuracy : 0.69 ; ROC_AUC : 0.69 ; F1 : 0.6893
Epoch 600:
Accuracy : 0.69 ; ROC_AUC : 0.69 ; F1 : 0.6894
Epoch 650:
Accuracy : 0.69 ; ROC_AUC : 0.69 ; F1 : 0.6894
Epoch 700:
Accuracy : 0.6906 ; ROC_AUC : 0.6906 ; F1 : 0.691
Epoch 750:
Accuracy : 0.6901 ; ROC_AUC : 0.6901 ; F1 : 0.6892
Epoch 800:
Accuracy : 0.6902 ; ROC_AUC : 0.6902 ; F1 : 0.6896
Epoch 850:
Accuracy : 0.6901 ; ROC_AUC : 0.6901 ; F1 : 0.6895
Epoch 900:
Accuracy : 0.6902 ; ROC_AUC : 0.6902 ; F1 : 0.6896
Epoch 950:
Accuracy : 0.6903 ; ROC_AUC : 0.6903 ; F1 : 0.6897
Epoch 1000:
Accuracy : 0.6905 ; ROC_AUC : 0.6905 ; F1 : 0.6904

Test data:
Accuracy : 0.6814 ; ROC_AUC : 0.6814 ; F1 : 0.6816
Training time: 2.297 seconds
Testing time: 2.2334 seconds


exp_name = f"Model1 selected"
expLog = get_results(expLog, exp_name, learning_rate, epochs, model, train_time, test_time, X_train_sel_tensor, y_train_sel, X_test_sel_tensor, y_test_sel)
expLog


%reload_ext tensorboard


tensorboard --logdir=runs

Reusing TensorBoard on port 6006 (pid 4280), started 0:00:10 ago. (Use '!kill 4280' to kill it.)


import torch.nn as nn

class EnhancedMLP(nn.Module):
    def __init__(self, input_size):
        super(EnhancedMLP, self).__init__()
        self.hl1 = nn.Linear(input_size, 512)
        self.bn1 = nn.BatchNorm1d(512)
        self.hl2 = nn.Linear(512, 256)
        self.bn2 = nn.BatchNorm1d(256)
        self.hl3 = nn.Linear(256, 128)
        self.bn3 = nn.BatchNorm1d(128)
        self.hl4 = nn.Linear(128, 64)
        self.bn4 = nn.BatchNorm1d(64)
        self.hl5 = nn.Linear(64, 32)
        self.bn5 = nn.BatchNorm1d(32)
        self.hl6 = nn.Linear(32, 1)
        self.activation = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.dropout = nn.Dropout(0.5)
    
    def forward(self, x):
        x = self.activation(self.bn1(self.hl1(x)))
        x = self.dropout(x)
        x = self.activation(self.bn2(self.hl2(x)))
        x = self.dropout(x)
        x = self.activation(self.bn3(self.hl3(x)))
        x = self.dropout(x)
        x = self.activation(self.bn4(self.hl4(x)))
        x = self.dropout(x)
        x = self.activation(self.bn5(self.hl5(x)))
        x = self.sigmoid(self.hl6(x))
        return x

model2 = EnhancedMLP(X_train_tensor.shape[1])


from torchsummary import summary

# Print summary of model architecture
summary(model2, input_size=(X_train_tensor.shape[1],), device='cpu')

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Linear-1                  [-1, 512]         125,952
       BatchNorm1d-2                  [-1, 512]           1,024
              ReLU-3                  [-1, 512]               0
           Dropout-4                  [-1, 512]               0
            Linear-5                  [-1, 256]         131,328
       BatchNorm1d-6                  [-1, 256]             512
              ReLU-7                  [-1, 256]               0
           Dropout-8                  [-1, 256]               0
            Linear-9                  [-1, 128]          32,896
      BatchNorm1d-10                  [-1, 128]             256
             ReLU-11                  [-1, 128]               0
          Dropout-12                  [-1, 128]               0
           Linear-13                   [-1, 64]           8,256
      BatchNorm1d-14                   [-1, 64]             128
             ReLU-15                   [-1, 64]               0
          Dropout-16                   [-1, 64]               0
           Linear-17                   [-1, 32]           2,080
      BatchNorm1d-18                   [-1, 32]              64
             ReLU-19                   [-1, 32]               0
           Linear-20                    [-1, 1]              33
          Sigmoid-21                    [-1, 1]               0
================================================================
Total params: 302,529
Trainable params: 302,529
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.03
Params size (MB): 1.15
Estimated Total Size (MB): 1.19
----------------------------------------------------------------


model = model2
learning_rate = 0.01
epochs = 1000
optimizer = Adam(model.parameters(), learning_rate)

# Training the model
start_time = time.time()
train_and_test(X_train_tensor, y_train_tensor, X_test_tensor, y_test, model, optimizer, writer, learning_rate, epochs)
train_time = np.round(time.time() - start_time, 4)

# Testing the model
start_time = time.time()
train_and_test(X_train_tensor, y_train_tensor, X_test_tensor, y_test, model, optimizer, writer, learning_rate, epochs)
test_time = np.round(time.time() - start_time, 4)

print(f'Training time: {train_time} seconds')
print(f'Testing time: {test_time} seconds')

Model Architecture:
EnhancedMLP(
  (hl1): Linear(in_features=245, out_features=512, bias=True)
  (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=512, out_features=256, bias=True)
  (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=256, out_features=128, bias=True)
  (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=128, out_features=64, bias=True)
  (bn4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=64, out_features=32, bias=True)
  (bn5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=32, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.7291 ; ROC_AUC : 0.7291 ; F1 : 0.7321
Epoch 100:
Accuracy : 0.7747 ; ROC_AUC : 0.7747 ; F1 : 0.7781
Epoch 150:
Accuracy : 0.8133 ; ROC_AUC : 0.8133 ; F1 : 0.8241
Epoch 200:
Accuracy : 0.8493 ; ROC_AUC : 0.8493 ; F1 : 0.8476
Epoch 250:
Accuracy : 0.8648 ; ROC_AUC : 0.8648 ; F1 : 0.8691
Epoch 300:
Accuracy : 0.881 ; ROC_AUC : 0.881 ; F1 : 0.878
Epoch 350:
Accuracy : 0.8864 ; ROC_AUC : 0.8864 ; F1 : 0.8859
Epoch 400:
Accuracy : 0.8921 ; ROC_AUC : 0.8921 ; F1 : 0.8936
Epoch 450:
Accuracy : 0.9012 ; ROC_AUC : 0.9012 ; F1 : 0.9009
Epoch 500:
Accuracy : 0.9074 ; ROC_AUC : 0.9074 ; F1 : 0.907
Epoch 550:
Accuracy : 0.9115 ; ROC_AUC : 0.9115 ; F1 : 0.9116
Epoch 600:
Accuracy : 0.912 ; ROC_AUC : 0.9119 ; F1 : 0.914
Epoch 650:
Accuracy : 0.9182 ; ROC_AUC : 0.9182 ; F1 : 0.9183
Epoch 700:
Accuracy : 0.9216 ; ROC_AUC : 0.9215 ; F1 : 0.9222
Epoch 750:
Accuracy : 0.9243 ; ROC_AUC : 0.9243 ; F1 : 0.9239
Epoch 800:
Accuracy : 0.9245 ; ROC_AUC : 0.9245 ; F1 : 0.9251
Epoch 850:
Accuracy : 0.9288 ; ROC_AUC : 0.9288 ; F1 : 0.9288
Epoch 900:
Accuracy : 0.9266 ; ROC_AUC : 0.9266 ; F1 : 0.9273
Epoch 950:
Accuracy : 0.9254 ; ROC_AUC : 0.9253 ; F1 : 0.9264
Epoch 1000:
Accuracy : 0.9292 ; ROC_AUC : 0.9292 ; F1 : 0.9293

Test data:
Accuracy : 0.638 ; ROC_AUC : 0.6382 ; F1 : 0.6726
Model Architecture:
EnhancedMLP(
  (hl1): Linear(in_features=245, out_features=512, bias=True)
  (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=512, out_features=256, bias=True)
  (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=256, out_features=128, bias=True)
  (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=128, out_features=64, bias=True)
  (bn4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=64, out_features=32, bias=True)
  (bn5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=32, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.9334 ; ROC_AUC : 0.9334 ; F1 : 0.9331
Epoch 100:
Accuracy : 0.9323 ; ROC_AUC : 0.9323 ; F1 : 0.932
Epoch 150:
Accuracy : 0.9337 ; ROC_AUC : 0.9337 ; F1 : 0.9342
Epoch 200:
Accuracy : 0.9333 ; ROC_AUC : 0.9333 ; F1 : 0.9336
Epoch 250:
Accuracy : 0.936 ; ROC_AUC : 0.936 ; F1 : 0.9358
Epoch 300:
Accuracy : 0.9383 ; ROC_AUC : 0.9383 ; F1 : 0.9382
Epoch 350:
Accuracy : 0.9364 ; ROC_AUC : 0.9364 ; F1 : 0.9363
Epoch 400:
Accuracy : 0.9371 ; ROC_AUC : 0.9371 ; F1 : 0.9372
Epoch 450:
Accuracy : 0.9402 ; ROC_AUC : 0.9402 ; F1 : 0.9403
Epoch 500:
Accuracy : 0.9397 ; ROC_AUC : 0.9397 ; F1 : 0.9396
Epoch 550:
Accuracy : 0.9365 ; ROC_AUC : 0.9365 ; F1 : 0.936
Epoch 600:
Accuracy : 0.9419 ; ROC_AUC : 0.9419 ; F1 : 0.9422
Epoch 650:
Accuracy : 0.9436 ; ROC_AUC : 0.9436 ; F1 : 0.9436
Epoch 700:
Accuracy : 0.9401 ; ROC_AUC : 0.9401 ; F1 : 0.9404
Epoch 750:
Accuracy : 0.9425 ; ROC_AUC : 0.9426 ; F1 : 0.9422
Epoch 800:
Accuracy : 0.9445 ; ROC_AUC : 0.9445 ; F1 : 0.9447
Epoch 850:
Accuracy : 0.9447 ; ROC_AUC : 0.9447 ; F1 : 0.945
Epoch 900:
Accuracy : 0.9458 ; ROC_AUC : 0.9458 ; F1 : 0.946
Epoch 950:
Accuracy : 0.9449 ; ROC_AUC : 0.9449 ; F1 : 0.945
Epoch 1000:
Accuracy : 0.945 ; ROC_AUC : 0.945 ; F1 : 0.9454

Test data:
Accuracy : 0.6346 ; ROC_AUC : 0.6349 ; F1 : 0.6661
Training time: 27.099 seconds
Testing time: 28.2518 seconds


exp_name = f"Model 2 Enhanced all "
expLog = get_results(expLog, exp_name, learning_rate, epochs, model, train_time, test_time, X_train_tensor, y_train, X_test_tensor, y_test)
expLog


%reload_ext tensorboard


tensorboard --logdir=runs

Reusing TensorBoard on port 6006 (pid 4280), started 0:01:12 ago. (Use '!kill 4280' to kill it.)


model2 = EnhancedMLP(X_train_tensor.shape[1])
model = model2
learning_rate = 0.001
epochs = 50
optimizer = Adam(model.parameters(), learning_rate)

# Training the model
start_time = time.time()
train_and_test(X_train_tensor, y_train_tensor, X_test_tensor, y_test, model, optimizer, writer, learning_rate, epochs)
train_time = np.round(time.time() - start_time, 4)

# Testing the model
start_time = time.time()
train_and_test(X_train_tensor, y_train_tensor, X_test_tensor, y_test, model, optimizer, writer, learning_rate, epochs)
test_time = np.round(time.time() - start_time, 4)

print(f'Training time: {train_time} seconds')
print(f'Testing time: {test_time} seconds')

exp_name = f"Model 2 enhanced 2"
expLog = get_results(expLog, exp_name, learning_rate, epochs, model, train_time, test_time, X_train_tensor, y_train, X_test_tensor, y_test)
expLog

Model Architecture:
EnhancedMLP(
  (hl1): Linear(in_features=245, out_features=512, bias=True)
  (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=512, out_features=256, bias=True)
  (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=256, out_features=128, bias=True)
  (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=128, out_features=64, bias=True)
  (bn4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=64, out_features=32, bias=True)
  (bn5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=32, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.698 ; ROC_AUC : 0.698 ; F1 : 0.6988

Test data:
Accuracy : 0.6811 ; ROC_AUC : 0.6811 ; F1 : 0.6796
Model Architecture:
EnhancedMLP(
  (hl1): Linear(in_features=245, out_features=512, bias=True)
  (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=512, out_features=256, bias=True)
  (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=256, out_features=128, bias=True)
  (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=128, out_features=64, bias=True)
  (bn4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=64, out_features=32, bias=True)
  (bn5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=32, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.7204 ; ROC_AUC : 0.7204 ; F1 : 0.7228

Test data:
Accuracy : 0.6806 ; ROC_AUC : 0.6807 ; F1 : 0.6925
Training time: 1.4786 seconds
Testing time: 1.407 seconds


%reload_ext tensorboard


tensorboard --logdir=runs

Reusing TensorBoard on port 6006 (pid 4280), started 0:01:28 ago. (Use '!kill 4280' to kill it.)


model2 = EnhancedMLP(X_train_sel_tensor.shape[1])
model = model2
learning_rate = 0.001
epochs = 50
optimizer = Adam(model.parameters(), learning_rate)

 #Training the model
start_time = time.time()
train_and_test(X_train_sel_tensor, y_train_sel_tensor, X_test_sel_tensor, y_test_sel, model, optimizer, writer, learning_rate, epochs)
train_time = np.round(time.time() - start_time, 4)

# Testing the model
start_time = time.time()
train_and_test(X_train_sel_tensor, y_train_sel_tensor, X_test_sel_tensor, y_test_sel, model, optimizer, writer, learning_rate, epochs)
test_time = np.round(time.time() - start_time, 4)

print(f'Training time: {train_time} seconds')
print(f'Testing time: {test_time} seconds')


exp_name = f"Model 2 enhanced and selected "
expLog = get_results(expLog, exp_name, learning_rate, epochs, model, train_time, test_time, X_train_sel_tensor, y_train_sel, X_test_sel_tensor, y_test_sel)
expLog

Model Architecture:
EnhancedMLP(
  (hl1): Linear(in_features=233, out_features=512, bias=True)
  (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=512, out_features=256, bias=True)
  (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=256, out_features=128, bias=True)
  (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=128, out_features=64, bias=True)
  (bn4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=64, out_features=32, bias=True)
  (bn5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=32, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.6971 ; ROC_AUC : 0.6971 ; F1 : 0.6968

Test data:
Accuracy : 0.6835 ; ROC_AUC : 0.6835 ; F1 : 0.6842
Model Architecture:
EnhancedMLP(
  (hl1): Linear(in_features=233, out_features=512, bias=True)
  (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=512, out_features=256, bias=True)
  (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=256, out_features=128, bias=True)
  (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=128, out_features=64, bias=True)
  (bn4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=64, out_features=32, bias=True)
  (bn5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=32, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.7176 ; ROC_AUC : 0.7176 ; F1 : 0.7177

Test data:
Accuracy : 0.6826 ; ROC_AUC : 0.6826 ; F1 : 0.6904
Training time: 1.4156 seconds
Testing time: 1.4354 seconds


%reload_ext tensorboard


tensorboard --logdir=runs

Reusing TensorBoard on port 6006 (pid 4280), started 0:01:32 ago. (Use '!kill 4280' to kill it.)


model2 = EnhancedMLP(X_train_sel_tensor.shape[1])
model = model2
learning_rate = 0.0005
epochs = 50
optimizer = Adam(model.parameters(), learning_rate)

 #Training the model
start_time = time.time()
train_and_test(X_train_sel_tensor, y_train_sel_tensor, X_test_sel_tensor, y_test_sel, model, optimizer, writer, learning_rate, epochs)
train_time = np.round(time.time() - start_time, 4)

# Testing the model
start_time = time.time()
train_and_test(X_train_sel_tensor, y_train_sel_tensor, X_test_sel_tensor, y_test_sel, model, optimizer, writer, learning_rate, epochs)
test_time = np.round(time.time() - start_time, 4)

print(f'Training time: {train_time} seconds')
print(f'Testing time: {test_time} seconds')

exp_name = f"Model 3 change learning rate and epochs and selected "
expLog = get_results(expLog, exp_name, learning_rate, epochs, model, train_time, test_time, X_train_sel_tensor, y_train_sel, X_test_sel_tensor, y_test_sel)
expLog

Model Architecture:
EnhancedMLP(
  (hl1): Linear(in_features=233, out_features=512, bias=True)
  (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=512, out_features=256, bias=True)
  (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=256, out_features=128, bias=True)
  (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=128, out_features=64, bias=True)
  (bn4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=64, out_features=32, bias=True)
  (bn5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=32, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.6871 ; ROC_AUC : 0.6871 ; F1 : 0.6853

Test data:
Accuracy : 0.6799 ; ROC_AUC : 0.6799 ; F1 : 0.6865
Model Architecture:
EnhancedMLP(
  (hl1): Linear(in_features=233, out_features=512, bias=True)
  (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=512, out_features=256, bias=True)
  (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=256, out_features=128, bias=True)
  (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=128, out_features=64, bias=True)
  (bn4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=64, out_features=32, bias=True)
  (bn5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=32, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.7015 ; ROC_AUC : 0.7015 ; F1 : 0.7036

Test data:
Accuracy : 0.6816 ; ROC_AUC : 0.6817 ; F1 : 0.6915
Training time: 1.4849 seconds
Testing time: 1.4059 seconds


%reload_ext tensorboard


tensorboard --logdir=runs

Reusing TensorBoard on port 6006 (pid 4280), started 0:01:51 ago. (Use '!kill 4280' to kill it.)


# Deep Wider 
import torch.nn as nn

class DeeperWiderMLP(nn.Module):
    def __init__(self, input_size):
        super(DeeperWiderMLP, self).__init__()
        self.hl1 = nn.Linear(input_size, 1024)
        self.bn1 = nn.BatchNorm1d(1024)
        self.hl2 = nn.Linear(1024, 512)
        self.bn2 = nn.BatchNorm1d(512)
        self.hl3 = nn.Linear(512, 256)
        self.bn3 = nn.BatchNorm1d(256)
        self.hl4 = nn.Linear(256, 128)
        self.bn4 = nn.BatchNorm1d(128)
        self.hl5 = nn.Linear(128, 64)
        self.bn5 = nn.BatchNorm1d(64)
        self.hl6 = nn.Linear(64, 32)
        self.bn6 = nn.BatchNorm1d(32)
        self.hl7 = nn.Linear(32, 16)
        self.bn7 = nn.BatchNorm1d(16)
        self.hl8 = nn.Linear(16, 1)
        self.activation = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.dropout = nn.Dropout(0.5)
    
    def forward(self, x):
        x = self.activation(self.bn1(self.hl1(x)))
        x = self.dropout(x)
        x = self.activation(self.bn2(self.hl2(x)))
        x = self.dropout(x)
        x = self.activation(self.bn3(self.hl3(x)))
        x = self.dropout(x)
        x = self.activation(self.bn4(self.hl4(x)))
        x = self.dropout(x)
        x = self.activation(self.bn5(self.hl5(x)))
        x = self.dropout(x)
        x = self.activation(self.bn6(self.hl6(x)))
        x = self.dropout(x)
        x = self.activation(self.bn7(self.hl7(x)))
        x = self.sigmoid(self.hl8(x))
        return x


from torchsummary import summary
model = DeeperWiderMLP(X_train_tensor.shape[1])
# Print summary of model architecture
summary(model, input_size=(X_train_tensor.shape[1],), device='cpu')

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Linear-1                 [-1, 1024]         251,904
       BatchNorm1d-2                 [-1, 1024]           2,048
              ReLU-3                 [-1, 1024]               0
           Dropout-4                 [-1, 1024]               0
            Linear-5                  [-1, 512]         524,800
       BatchNorm1d-6                  [-1, 512]           1,024
              ReLU-7                  [-1, 512]               0
           Dropout-8                  [-1, 512]               0
            Linear-9                  [-1, 256]         131,328
      BatchNorm1d-10                  [-1, 256]             512
             ReLU-11                  [-1, 256]               0
          Dropout-12                  [-1, 256]               0
           Linear-13                  [-1, 128]          32,896
      BatchNorm1d-14                  [-1, 128]             256
             ReLU-15                  [-1, 128]               0
          Dropout-16                  [-1, 128]               0
           Linear-17                   [-1, 64]           8,256
      BatchNorm1d-18                   [-1, 64]             128
             ReLU-19                   [-1, 64]               0
          Dropout-20                   [-1, 64]               0
           Linear-21                   [-1, 32]           2,080
      BatchNorm1d-22                   [-1, 32]              64
             ReLU-23                   [-1, 32]               0
          Dropout-24                   [-1, 32]               0
           Linear-25                   [-1, 16]             528
      BatchNorm1d-26                   [-1, 16]              32
             ReLU-27                   [-1, 16]               0
           Linear-28                    [-1, 1]              17
          Sigmoid-29                    [-1, 1]               0
================================================================
Total params: 955,873
Trainable params: 955,873
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.06
Params size (MB): 3.65
Estimated Total Size (MB): 3.71
----------------------------------------------------------------


model = DeeperWiderMLP(X_train_tensor.shape[1])
model = model
learning_rate = 0.001
epochs = 50
optimizer = Adam(model.parameters(), learning_rate)

# Training the model
start_time = time.time()
train_and_test(X_train_tensor, y_train_tensor, X_test_tensor, y_test, model, optimizer, writer, learning_rate, epochs)
train_time = np.round(time.time() - start_time, 4)

# Testing the model
start_time = time.time()
train_and_test(X_train_tensor, y_train_tensor, X_test_tensor, y_test, model, optimizer, writer, learning_rate, epochs)
test_time = np.round(time.time() - start_time, 4)

print(f'Training time: {train_time} seconds')
print(f'Testing time: {test_time} seconds')

Model Architecture:
DeeperWiderMLP(
  (hl1): Linear(in_features=245, out_features=1024, bias=True)
  (bn1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=1024, out_features=512, bias=True)
  (bn2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=512, out_features=256, bias=True)
  (bn3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=256, out_features=128, bias=True)
  (bn4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=128, out_features=64, bias=True)
  (bn5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=64, out_features=32, bias=True)
  (bn6): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl7): Linear(in_features=32, out_features=16, bias=True)
  (bn7): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl8): Linear(in_features=16, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.6977 ; ROC_AUC : 0.6977 ; F1 : 0.6927

Test data:
Accuracy : 0.6839 ; ROC_AUC : 0.6838 ; F1 : 0.6756
Model Architecture:
DeeperWiderMLP(
  (hl1): Linear(in_features=245, out_features=1024, bias=True)
  (bn1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=1024, out_features=512, bias=True)
  (bn2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=512, out_features=256, bias=True)
  (bn3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=256, out_features=128, bias=True)
  (bn4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=128, out_features=64, bias=True)
  (bn5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=64, out_features=32, bias=True)
  (bn6): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl7): Linear(in_features=32, out_features=16, bias=True)
  (bn7): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl8): Linear(in_features=16, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.7298 ; ROC_AUC : 0.7298 ; F1 : 0.7267

Test data:
Accuracy : 0.6805 ; ROC_AUC : 0.6804 ; F1 : 0.6738
Training time: 3.6939 seconds
Testing time: 3.6335 seconds


exp_name = f"Model 4 deepwide all"
expLog = get_results(expLog, exp_name, learning_rate, epochs, model, train_time, test_time, X_train_tensor, y_train, X_test_tensor, y_test)
expLog


%reload_ext tensorboard


tensorboard --logdir=runs

Reusing TensorBoard on port 6006 (pid 4280), started 0:02:27 ago. (Use '!kill 4280' to kill it.)


model = DeeperWiderMLP(X_train_sel_tensor.shape[1])
model = model
learning_rate = 0.001
epochs = 50
optimizer = Adam(model.parameters(), learning_rate)


 #Training the model
start_time = time.time()
train_and_test(X_train_sel_tensor, y_train_sel_tensor, X_test_sel_tensor, y_test_sel, model, optimizer, writer, learning_rate, epochs)
train_time = np.round(time.time() - start_time, 4)

# Testing the model
start_time = time.time()
train_and_test(X_train_sel_tensor, y_train_sel_tensor, X_test_sel_tensor, y_test_sel, model, optimizer, writer, learning_rate, epochs)
test_time = np.round(time.time() - start_time, 4)

print(f'Training time: {train_time} seconds')
print(f'Testing time: {test_time} seconds')

Model Architecture:
DeeperWiderMLP(
  (hl1): Linear(in_features=233, out_features=1024, bias=True)
  (bn1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=1024, out_features=512, bias=True)
  (bn2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=512, out_features=256, bias=True)
  (bn3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=256, out_features=128, bias=True)
  (bn4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=128, out_features=64, bias=True)
  (bn5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=64, out_features=32, bias=True)
  (bn6): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl7): Linear(in_features=32, out_features=16, bias=True)
  (bn7): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl8): Linear(in_features=16, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.6959 ; ROC_AUC : 0.6959 ; F1 : 0.6954

Test data:
Accuracy : 0.6802 ; ROC_AUC : 0.6802 ; F1 : 0.6873
Model Architecture:
DeeperWiderMLP(
  (hl1): Linear(in_features=233, out_features=1024, bias=True)
  (bn1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl2): Linear(in_features=1024, out_features=512, bias=True)
  (bn2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl3): Linear(in_features=512, out_features=256, bias=True)
  (bn3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl4): Linear(in_features=256, out_features=128, bias=True)
  (bn4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl5): Linear(in_features=128, out_features=64, bias=True)
  (bn5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl6): Linear(in_features=64, out_features=32, bias=True)
  (bn6): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl7): Linear(in_features=32, out_features=16, bias=True)
  (bn7): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (hl8): Linear(in_features=16, out_features=1, bias=True)
  (activation): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.5, inplace=False)
) 

Training the model:
Epoch 50:
Accuracy : 0.73 ; ROC_AUC : 0.73 ; F1 : 0.7308

Test data:
Accuracy : 0.6806 ; ROC_AUC : 0.6807 ; F1 : 0.7029
Training time: 3.6692 seconds
Testing time: 3.6169 seconds


exp_name = f"Model 4 deepwide selected "
expLog = get_results(expLog, exp_name, learning_rate, epochs, model, train_time, test_time, X_train_sel_tensor, y_train_sel, X_test_sel_tensor, y_test_sel)
expLog


%reload_ext tensorboard


tensorboard --logdir=runs

Reusing TensorBoard on port 6006 (pid 4280), started 0:02:37 ago. (Use '!kill 4280' to kill it.)


import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
import torch.nn as nn

class DeeperWiderMLP(nn.Module):
    def __init__(self, input_size):
        super(DeeperWiderMLP, self).__init__()
        self.hl1 = nn.Linear(input_size, 1024)
        self.bn1 = nn.BatchNorm1d(1024)
        self.hl2 = nn.Linear(1024, 512)
        self.bn2 = nn.BatchNorm1d(512)
        self.hl3 = nn.Linear(512, 256)
        self.bn3 = nn.BatchNorm1d(256)
        self.hl4 = nn.Linear(256, 128)
        self.bn4 = nn.BatchNorm1d(128)
        self.hl5 = nn.Linear(128, 64)
        self.bn5 = nn.BatchNorm1d(64)
        self.hl6 = nn.Linear(64, 32)
        self.bn6 = nn.BatchNorm1d(32)
        self.hl7 = nn.Linear(32, 16)
        self.bn7 = nn.BatchNorm1d(16)
        self.hl8 = nn.Linear(16, 1)
        self.activation = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.dropout = nn.Dropout(0.5)
    
    def forward(self, x):
        x = self.activation(self.bn1(self.hl1(x)))
        x = self.dropout(x)
        x = self.activation(self.bn2(self.hl2(x)))
        x = self.dropout(x)
        x = self.activation(self.bn3(self.hl3(x)))
        x = self.dropout(x)
        x = self.activation(self.bn4(self.hl4(x)))
        x = self.dropout(x)
        x = self.activation(self.bn5(self.hl5(x)))
        x = self.dropout(x)
        x = self.activation(self.bn6(self.hl6(x)))
        x = self.dropout(x)
        x = self.activation(self.bn7(self.hl7(x)))
        x = self.sigmoid(self.hl8(x))
        return x

# Define hyperparameters
learning_rate = 0.001
num_epochs = 20
batch_size =  64
dropout_rate = 0.4 

# Define the model
model = DeeperWiderMLP(X_train_tensor.shape[1])
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Define the loss function
criterion = nn.BCELoss()

# Define the data loaders
train_loader = DataLoader(TensorDataset(X_train_tensor, y_train_tensor), batch_size=batch_size, shuffle=True)
test_loader = DataLoader(TensorDataset(X_test_tensor, y_test_tensor), batch_size=batch_size)

from sklearn.metrics import f1_score, roc_auc_score

# Train and evaluate the model
for epoch in range(num_epochs):
    # Train the model
    train_loss = 0
    model.train()
    for batch_x, batch_y in train_loader:
        optimizer.zero_grad()
        batch_y_pred = model(batch_x)
        loss = criterion(batch_y_pred, batch_y)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * batch_x.size(0)
    train_loss /= len(train_loader.dataset)
    
    # Evaluate the model
    test_loss = 0
    test_acc = 0
    test_f1 = 0
    test_auc = 0
    true_labels = []
    pred_labels = []
    model.eval()
    with torch.no_grad():
        for batch_x, batch_y in test_loader:
            batch_y_pred = model(batch_x)
            loss = criterion(batch_y_pred, batch_y)
            test_loss += loss.item() * batch_x.size(0)
            
            true_labels.extend(batch_y.numpy())
            pred_labels.extend((batch_y_pred > 0.5).float().numpy())
            
    test_loss /= len(test_loader.dataset)
    test_acc = (sum([1 for true_label, pred_label in zip(true_labels, pred_labels) if true_label == pred_label])) / len(true_labels)
    test_f1 = f1_score(true_labels, pred_labels)
    test_auc = roc_auc_score(true_labels, pred_labels)

    # Print the results for this epoch
    print(f"Epoch {epoch+1}/{num_epochs} - Train loss: {train_loss:.4f} - Test loss: {test_loss:.4f} - Test accuracy: {test_acc:.4f} - Test F1 score: {test_f1:.4f} - Test AUC: {test_auc:.4f}")

# Adjust the learning rate if necessary

if epoch > 0 and epoch % 5 == 0:
    for param_group in optimizer.param_groups:
        param_group['lr'] *= 0.1

# Adjust the dropout rate if necessary
if epoch > 0 and epoch % 5 == 0:
    model.dropout.p = dropout_rate

print("Training complete.")

Epoch 1/20 - Train loss: 0.6535 - Test loss: 0.6252 - Test accuracy: 0.6641 - Test F1 score: 0.6831 - Test AUC: 0.6643
Epoch 2/20 - Train loss: 0.6167 - Test loss: 0.6110 - Test accuracy: 0.6734 - Test F1 score: 0.6944 - Test AUC: 0.6736
Epoch 3/20 - Train loss: 0.6072 - Test loss: 0.6049 - Test accuracy: 0.6765 - Test F1 score: 0.7025 - Test AUC: 0.6768
Epoch 4/20 - Train loss: 0.6030 - Test loss: 0.6047 - Test accuracy: 0.6750 - Test F1 score: 0.6913 - Test AUC: 0.6752
Epoch 5/20 - Train loss: 0.6002 - Test loss: 0.6041 - Test accuracy: 0.6794 - Test F1 score: 0.7047 - Test AUC: 0.6796
Epoch 6/20 - Train loss: 0.5967 - Test loss: 0.6030 - Test accuracy: 0.6793 - Test F1 score: 0.6894 - Test AUC: 0.6793
Epoch 7/20 - Train loss: 0.5948 - Test loss: 0.6023 - Test accuracy: 0.6825 - Test F1 score: 0.6908 - Test AUC: 0.6825
Epoch 8/20 - Train loss: 0.5932 - Test loss: 0.6023 - Test accuracy: 0.6772 - Test F1 score: 0.6977 - Test AUC: 0.6774
Epoch 9/20 - Train loss: 0.5903 - Test loss: 0.6036 - Test accuracy: 0.6788 - Test F1 score: 0.6962 - Test AUC: 0.6789
Epoch 10/20 - Train loss: 0.5891 - Test loss: 0.6008 - Test accuracy: 0.6818 - Test F1 score: 0.6810 - Test AUC: 0.6818
Epoch 11/20 - Train loss: 0.5871 - Test loss: 0.6022 - Test accuracy: 0.6799 - Test F1 score: 0.6911 - Test AUC: 0.6800
Epoch 12/20 - Train loss: 0.5843 - Test loss: 0.6030 - Test accuracy: 0.6797 - Test F1 score: 0.6733 - Test AUC: 0.6796
Epoch 13/20 - Train loss: 0.5829 - Test loss: 0.6031 - Test accuracy: 0.6776 - Test F1 score: 0.6905 - Test AUC: 0.6777
Epoch 14/20 - Train loss: 0.5801 - Test loss: 0.6027 - Test accuracy: 0.6739 - Test F1 score: 0.6496 - Test AUC: 0.6738
Epoch 15/20 - Train loss: 0.5781 - Test loss: 0.6034 - Test accuracy: 0.6799 - Test F1 score: 0.6782 - Test AUC: 0.6799
Epoch 16/20 - Train loss: 0.5730 - Test loss: 0.6049 - Test accuracy: 0.6770 - Test F1 score: 0.6807 - Test AUC: 0.6771
Epoch 17/20 - Train loss: 0.5734 - Test loss: 0.6054 - Test accuracy: 0.6808 - Test F1 score: 0.7051 - Test AUC: 0.6810
Epoch 18/20 - Train loss: 0.5705 - Test loss: 0.6044 - Test accuracy: 0.6759 - Test F1 score: 0.6670 - Test AUC: 0.6759
Epoch 19/20 - Train loss: 0.5696 - Test loss: 0.6037 - Test accuracy: 0.6820 - Test F1 score: 0.6935 - Test AUC: 0.6821
Epoch 20/20 - Train loss: 0.5643 - Test loss: 0.6049 - Test accuracy: 0.6761 - Test F1 score: 0.6843 - Test AUC: 0.6762
Training complete.


# export the DataFrame to a CSV file
#df.to_csv('expLog.csv', index=False)

# load the CSV file back into a DataFrame
expLog= pd.read_csv('expLog.csv')


expLog

SK_ID_CURR,TARGET
100001,0.1
100005,0.9
100013,0.2
etc.


# Predicting class scores using the model
nn_test_class_scores = model(X_kaggle_test_sel_tensor).cpu().data.numpy().reshape(1, -1)[0]


# Creating a dataframe
nn_submit_df = X_kaggle_test[['SK_ID_CURR']]
nn_submit_df['TARGET'] = nn_test_class_scores

# Saving the dataframe into csv
file_name = "Deepwide3"
#nn_submit_df.to_csv(f"/content/drive/My Drive/Colab Notebooks/submissions/{file_name}.csv",index=False)
nn_submit_df.to_csv(f"{file_name}.csv",index=False)


# Kaggle Submission
! kaggle competitions submit -c home-credit-default-risk -f Deepwide3.csv -m "submission_deep(ak)_learning"

Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /Users/deepak/.kaggle/kaggle.json'
100%|█████████████████████████████████████████| 838k/838k [00:01<00:00, 647kB/s]
Successfully submitted to Home Credit Default Risk


from IPython.display import Image
Image(filename='kaggle.png')

	SK_ID_CURR	TARGET	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	...	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	307511.000000	307511.000000	307511.000000	3.075110e+05	3.075110e+05	307499.000000	3.072330e+05	307511.000000	307511.000000	307511.000000	...	307511.000000	307511.000000	307511.000000	307511.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000
mean	278180.518577	0.080729	0.417052	1.687979e+05	5.990260e+05	27108.573909	5.383962e+05	0.020868	-16036.995067	63815.045904	...	0.008130	0.000595	0.000507	0.000335	0.006402	0.007000	0.034362	0.267395	0.265474	1.899974
std	102790.175348	0.272419	0.722121	2.371231e+05	4.024908e+05	14493.737315	3.694465e+05	0.013831	4363.988632	141275.766519	...	0.089798	0.024387	0.022518	0.018299	0.083849	0.110757	0.204685	0.916002	0.794056	1.869295
min	100002.000000	0.000000	0.000000	2.565000e+04	4.500000e+04	1615.500000	4.050000e+04	0.000290	-25229.000000	-17912.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	189145.500000	0.000000	0.000000	1.125000e+05	2.700000e+05	16524.000000	2.385000e+05	0.010006	-19682.000000	-2760.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	278202.000000	0.000000	0.000000	1.471500e+05	5.135310e+05	24903.000000	4.500000e+05	0.018850	-15750.000000	-1213.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
75%	367142.500000	0.000000	1.000000	2.025000e+05	8.086500e+05	34596.000000	6.795000e+05	0.028663	-12413.000000	-289.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000
max	456255.000000	1.000000	19.000000	1.170000e+08	4.050000e+06	258025.500000	4.050000e+06	0.072508	-7489.000000	365243.000000	...	1.000000	1.000000	1.000000	1.000000	4.000000	9.000000	8.000000	27.000000	261.000000	25.000000

	SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	...	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	307511.000000	307511.000000	307511	307511	307511	307511	307511.000000	3.075110e+05	3.075110e+05	307499.000000	...	307511.000000	307511.000000	307511.000000	307511.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000
unique	NaN	NaN	2	3	2	2	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
top	NaN	NaN	Cash loans	F	N	Y	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
freq	NaN	NaN	278232	202448	202924	213312	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
mean	278180.518577	0.080729	NaN	NaN	NaN	NaN	0.417052	1.687979e+05	5.990260e+05	27108.573909	...	0.008130	0.000595	0.000507	0.000335	0.006402	0.007000	0.034362	0.267395	0.265474	1.899974
std	102790.175348	0.272419	NaN	NaN	NaN	NaN	0.722121	2.371231e+05	4.024908e+05	14493.737315	...	0.089798	0.024387	0.022518	0.018299	0.083849	0.110757	0.204685	0.916002	0.794056	1.869295
min	100002.000000	0.000000	NaN	NaN	NaN	NaN	0.000000	2.565000e+04	4.500000e+04	1615.500000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	189145.500000	0.000000	NaN	NaN	NaN	NaN	0.000000	1.125000e+05	2.700000e+05	16524.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	278202.000000	0.000000	NaN	NaN	NaN	NaN	0.000000	1.471500e+05	5.135310e+05	24903.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
75%	367142.500000	0.000000	NaN	NaN	NaN	NaN	1.000000	2.025000e+05	8.086500e+05	34596.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000
max	456255.000000	1.000000	NaN	NaN	NaN	NaN	19.000000	1.170000e+08	4.050000e+06	258025.500000	...	1.000000	1.000000	1.000000	1.000000	4.000000	9.000000	8.000000	27.000000	261.000000	25.000000

	SK_ID_CURR	TARGET	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	...	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
SK_ID_CURR	1.000000	-0.002108	-0.001129	-0.001820	-0.000343	-0.000433	-0.000232	0.000849	-0.001500	0.001366	...	0.000509	0.000167	0.001073	0.000282	-0.002672	-0.002193	0.002099	0.000485	0.001025	0.004659
TARGET	-0.002108	1.000000	0.019187	-0.003982	-0.030369	-0.012817	-0.039645	-0.037227	0.078239	-0.044932	...	-0.007952	-0.001358	0.000215	0.003709	0.000930	0.002704	0.000788	-0.012462	-0.002022	0.019930
CNT_CHILDREN	-0.001129	0.019187	1.000000	0.012882	0.002145	0.021374	-0.001827	-0.025573	0.330938	-0.239818	...	0.004031	0.000864	0.000988	-0.002450	-0.000410	-0.000366	-0.002436	-0.010808	-0.007836	-0.041550
AMT_INCOME_TOTAL	-0.001820	-0.003982	0.012882	1.000000	0.156870	0.191657	0.159610	0.074796	0.027261	-0.064223	...	0.003130	0.002408	0.000242	-0.000589	0.000709	0.002944	0.002387	0.024700	0.004859	0.011690
AMT_CREDIT	-0.000343	-0.030369	0.002145	0.156870	1.000000	0.770138	0.986968	0.099738	-0.055436	-0.066838	...	0.034329	0.021082	0.031023	-0.016148	-0.003906	0.004238	-0.001275	0.054451	0.015925	-0.048448
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
AMT_REQ_CREDIT_BUREAU_DAY	-0.002193	0.002704	-0.000366	0.002944	0.004238	0.002185	0.004677	0.001399	0.002255	0.000472	...	0.013281	0.001126	-0.000120	-0.001130	0.230374	1.000000	0.217412	-0.005258	-0.004416	-0.003355
AMT_REQ_CREDIT_BUREAU_WEEK	0.002099	0.000788	-0.002436	0.002387	-0.001275	0.013881	-0.001007	-0.002149	-0.001336	0.003072	...	-0.004640	-0.001275	-0.001770	0.000081	0.004706	0.217412	1.000000	-0.014096	-0.015115	0.018917
AMT_REQ_CREDIT_BUREAU_MON	0.000485	-0.012462	-0.010808	0.024700	0.054451	0.039148	0.056422	0.078607	0.001372	-0.034457	...	-0.001565	-0.002729	0.001285	-0.003612	-0.000018	-0.005258	-0.014096	1.000000	-0.007789	-0.004975
AMT_REQ_CREDIT_BUREAU_QRT	0.001025	-0.002022	-0.007836	0.004859	0.015925	0.010124	0.016432	-0.001279	-0.011799	0.015345	...	-0.005125	-0.001575	-0.001010	-0.002004	-0.002716	-0.004416	-0.015115	-0.007789	1.000000	0.076208
AMT_REQ_CREDIT_BUREAU_YEAR	0.004659	0.019930	-0.041550	0.011690	-0.048448	-0.011320	-0.050998	0.001003	-0.071983	0.049988	...	-0.047432	-0.007009	-0.012126	-0.005457	-0.004597	-0.003355	0.018917	-0.004975	0.076208	1.000000

	Percent	Train Missing Count
COMMONAREA_MEDI	69.87	214865
COMMONAREA_AVG	69.87	214865
COMMONAREA_MODE	69.87	214865
NONLIVINGAPARTMENTS_MODE	69.43	213514
NONLIVINGAPARTMENTS_AVG	69.43	213514
NONLIVINGAPARTMENTS_MEDI	69.43	213514
FONDKAPREMONT_MODE	68.39	210295
LIVINGAPARTMENTS_MODE	68.35	210199
LIVINGAPARTMENTS_AVG	68.35	210199
LIVINGAPARTMENTS_MEDI	68.35	210199
FLOORSMIN_AVG	67.85	208642
FLOORSMIN_MODE	67.85	208642
FLOORSMIN_MEDI	67.85	208642
YEARS_BUILD_MEDI	66.50	204488
YEARS_BUILD_MODE	66.50	204488
YEARS_BUILD_AVG	66.50	204488
OWN_CAR_AGE	65.99	202929
LANDAREA_MEDI	59.38	182590
LANDAREA_MODE	59.38	182590
LANDAREA_AVG	59.38	182590

	SK_ID_CURR	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	...	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	48744.000000	48744.000000	4.874400e+04	4.874400e+04	48720.000000	4.874400e+04	48744.000000	48744.000000	48744.000000	48744.000000	...	48744.000000	48744.0	48744.0	48744.0	42695.000000	42695.000000	42695.000000	42695.000000	42695.000000	42695.000000
mean	277796.676350	0.397054	1.784318e+05	5.167404e+05	29426.240209	4.626188e+05	0.021226	-16068.084605	67485.366322	-4967.652716	...	0.001559	0.0	0.0	0.0	0.002108	0.001803	0.002787	0.009299	0.546902	1.983769
std	103169.547296	0.709047	1.015226e+05	3.653970e+05	16016.368315	3.367102e+05	0.014428	4325.900393	144348.507136	3552.612035	...	0.039456	0.0	0.0	0.0	0.046373	0.046132	0.054037	0.110924	0.693305	1.838873
min	100001.000000	0.000000	2.694150e+04	4.500000e+04	2295.000000	4.500000e+04	0.000253	-25195.000000	-17463.000000	-23722.000000	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	188557.750000	0.000000	1.125000e+05	2.606400e+05	17973.000000	2.250000e+05	0.010006	-19637.000000	-2910.000000	-7459.250000	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	277549.000000	0.000000	1.575000e+05	4.500000e+05	26199.000000	3.960000e+05	0.018850	-15785.000000	-1293.000000	-4490.000000	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000
75%	367555.500000	1.000000	2.250000e+05	6.750000e+05	37390.500000	6.300000e+05	0.028663	-12496.000000	-296.000000	-1901.000000	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	1.000000	3.000000
max	456250.000000	20.000000	4.410000e+06	2.245500e+06	180576.000000	2.245500e+06	0.072508	-7338.000000	365243.000000	0.000000	...	1.000000	0.0	0.0	0.0	2.000000	2.000000	2.000000	6.000000	7.000000	17.000000

	SK_ID_CURR	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	...	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	48744.000000	48744	48744	48744	48744	48744.000000	4.874400e+04	4.874400e+04	48720.000000	4.874400e+04	...	48744.000000	48744.0	48744.0	48744.0	42695.000000	42695.000000	42695.000000	42695.000000	42695.000000	42695.000000
unique	NaN	2	2	2	2	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
top	NaN	Cash loans	F	N	Y	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
freq	NaN	48305	32678	32311	33658	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
mean	277796.676350	NaN	NaN	NaN	NaN	0.397054	1.784318e+05	5.167404e+05	29426.240209	4.626188e+05	...	0.001559	0.0	0.0	0.0	0.002108	0.001803	0.002787	0.009299	0.546902	1.983769
std	103169.547296	NaN	NaN	NaN	NaN	0.709047	1.015226e+05	3.653970e+05	16016.368315	3.367102e+05	...	0.039456	0.0	0.0	0.0	0.046373	0.046132	0.054037	0.110924	0.693305	1.838873
min	100001.000000	NaN	NaN	NaN	NaN	0.000000	2.694150e+04	4.500000e+04	2295.000000	4.500000e+04	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	188557.750000	NaN	NaN	NaN	NaN	0.000000	1.125000e+05	2.606400e+05	17973.000000	2.250000e+05	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	277549.000000	NaN	NaN	NaN	NaN	0.000000	1.575000e+05	4.500000e+05	26199.000000	3.960000e+05	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000
75%	367555.500000	NaN	NaN	NaN	NaN	1.000000	2.250000e+05	6.750000e+05	37390.500000	6.300000e+05	...	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	1.000000	3.000000
max	456250.000000	NaN	NaN	NaN	NaN	20.000000	4.410000e+06	2.245500e+06	180576.000000	2.245500e+06	...	1.000000	0.0	0.0	0.0	2.000000	2.000000	2.000000	6.000000	7.000000	17.000000

	SK_ID_CURR	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	...	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
SK_ID_CURR	1.000000	0.000635	0.001278	0.005014	0.007112	0.005097	0.003324	0.002325	-0.000845	0.001032	...	-0.006286	NaN	NaN	NaN	-0.000307	0.001083	0.001178	0.000430	-0.002092	0.003457
CNT_CHILDREN	0.000635	1.000000	0.038962	0.027840	0.056770	0.025507	-0.015231	0.317877	-0.238319	0.175054	...	-0.000862	NaN	NaN	NaN	0.006362	0.001539	0.007523	-0.008337	0.029006	-0.039265
AMT_INCOME_TOTAL	0.001278	0.038962	1.000000	0.396572	0.457833	0.401995	0.199773	0.054400	-0.154619	0.067973	...	-0.006624	NaN	NaN	NaN	0.010227	0.004989	-0.002867	0.008691	0.007410	0.003281
AMT_CREDIT	0.005014	0.027840	0.396572	1.000000	0.777733	0.988056	0.135694	-0.046169	-0.083483	0.030740	...	-0.000197	NaN	NaN	NaN	-0.001092	0.004882	0.002904	-0.000156	-0.007750	-0.034533
AMT_ANNUITY	0.007112	0.056770	0.457833	0.777733	1.000000	0.787033	0.150864	0.047859	-0.137772	0.064450	...	-0.010762	NaN	NaN	NaN	0.008428	0.006681	0.003085	0.005695	0.012443	-0.044901
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
AMT_REQ_CREDIT_BUREAU_DAY	0.001083	0.001539	0.004989	0.004882	0.006681	0.004865	-0.011773	-0.000386	-0.000785	-0.000152	...	-0.001515	NaN	NaN	NaN	0.151506	1.000000	0.035567	0.005877	0.006509	0.002002
AMT_REQ_CREDIT_BUREAU_WEEK	0.001178	0.007523	-0.002867	0.002904	0.003085	0.003358	-0.008321	0.012422	-0.014058	0.008692	...	0.009205	NaN	NaN	NaN	-0.002345	0.035567	1.000000	0.054291	0.024957	-0.000252
AMT_REQ_CREDIT_BUREAU_MON	0.000430	-0.008337	0.008691	-0.000156	0.005695	-0.000254	0.000105	0.014094	-0.013891	0.007414	...	-0.003248	NaN	NaN	NaN	0.023510	0.005877	0.054291	1.000000	0.005446	0.026118
AMT_REQ_CREDIT_BUREAU_QRT	-0.002092	0.029006	0.007410	-0.007750	0.012443	-0.008490	-0.026650	0.088752	-0.044351	0.046011	...	-0.010480	NaN	NaN	NaN	-0.003075	0.006509	0.024957	0.005446	1.000000	-0.013081
AMT_REQ_CREDIT_BUREAU_YEAR	0.003457	-0.039265	0.003281	-0.034533	-0.044901	-0.036227	0.001015	-0.095551	0.064698	-0.036887	...	-0.009864	NaN	NaN	NaN	0.011938	0.002002	-0.000252	0.026118	-0.013081	1.000000

	Percent	Train Missing Count
COMMONAREA_AVG	68.72	33495
COMMONAREA_MODE	68.72	33495
COMMONAREA_MEDI	68.72	33495
NONLIVINGAPARTMENTS_AVG	68.41	33347
NONLIVINGAPARTMENTS_MODE	68.41	33347
NONLIVINGAPARTMENTS_MEDI	68.41	33347
FONDKAPREMONT_MODE	67.28	32797
LIVINGAPARTMENTS_AVG	67.25	32780
LIVINGAPARTMENTS_MODE	67.25	32780
LIVINGAPARTMENTS_MEDI	67.25	32780
FLOORSMIN_MEDI	66.61	32466
FLOORSMIN_AVG	66.61	32466
FLOORSMIN_MODE	66.61	32466
OWN_CAR_AGE	66.29	32312
YEARS_BUILD_AVG	65.28	31818
YEARS_BUILD_MEDI	65.28	31818
YEARS_BUILD_MODE	65.28	31818
LANDAREA_MEDI	57.96	28254
LANDAREA_AVG	57.96	28254
LANDAREA_MODE	57.96	28254

	SK_ID_CURR	SK_ID_BUREAU	DAYS_CREDIT	CREDIT_DAY_OVERDUE	DAYS_CREDIT_ENDDATE	DAYS_ENDDATE_FACT	AMT_CREDIT_MAX_OVERDUE	CNT_CREDIT_PROLONG	AMT_CREDIT_SUM	AMT_CREDIT_SUM_DEBT	AMT_CREDIT_SUM_LIMIT	AMT_CREDIT_SUM_OVERDUE	DAYS_CREDIT_UPDATE	AMT_ANNUITY
count	1.716428e+06	1.716428e+06	1.716428e+06	1.716428e+06	1.610875e+06	1.082775e+06	5.919400e+05	1.716428e+06	1.716415e+06	1.458759e+06	1.124648e+06	1.716428e+06	1.716428e+06	4.896370e+05
mean	2.782149e+05	5.924434e+06	-1.142108e+03	8.181666e-01	5.105174e+02	-1.017437e+03	3.825418e+03	6.410406e-03	3.549946e+05	1.370851e+05	6.229515e+03	3.791276e+01	-5.937483e+02	1.571276e+04
std	1.029386e+05	5.322657e+05	7.951649e+02	3.654443e+01	4.994220e+03	7.140106e+02	2.060316e+05	9.622391e-02	1.149811e+06	6.774011e+05	4.503203e+04	5.937650e+03	7.207473e+02	3.258269e+05
min	1.000010e+05	5.000000e+06	-2.922000e+03	0.000000e+00	-4.206000e+04	-4.202300e+04	0.000000e+00	0.000000e+00	0.000000e+00	-4.705600e+06	-5.864061e+05	0.000000e+00	-4.194700e+04	0.000000e+00
25%	1.888668e+05	5.463954e+06	-1.666000e+03	0.000000e+00	-1.138000e+03	-1.489000e+03	0.000000e+00	0.000000e+00	5.130000e+04	0.000000e+00	0.000000e+00	0.000000e+00	-9.080000e+02	0.000000e+00
50%	2.780550e+05	5.926304e+06	-9.870000e+02	0.000000e+00	-3.300000e+02	-8.970000e+02	0.000000e+00	0.000000e+00	1.255185e+05	0.000000e+00	0.000000e+00	0.000000e+00	-3.950000e+02	0.000000e+00
75%	3.674260e+05	6.385681e+06	-4.740000e+02	0.000000e+00	4.740000e+02	-4.250000e+02	0.000000e+00	0.000000e+00	3.150000e+05	4.015350e+04	0.000000e+00	0.000000e+00	-3.300000e+01	1.350000e+04
max	4.562550e+05	6.843457e+06	0.000000e+00	2.792000e+03	3.119900e+04	0.000000e+00	1.159872e+08	9.000000e+00	5.850000e+08	1.701000e+08	4.705600e+06	3.756681e+06	3.720000e+02	1.184534e+08

	SK_ID_CURR	SK_ID_BUREAU	DAYS_CREDIT	CREDIT_DAY_OVERDUE	DAYS_CREDIT_ENDDATE	DAYS_ENDDATE_FACT	AMT_CREDIT_MAX_OVERDUE	CNT_CREDIT_PROLONG	AMT_CREDIT_SUM	AMT_CREDIT_SUM_DEBT	AMT_CREDIT_SUM_LIMIT	AMT_CREDIT_SUM_OVERDUE	DAYS_CREDIT_UPDATE	AMT_ANNUITY
SK_ID_CURR	1.000000	0.000135	0.000266	0.000283	0.000456	-0.000648	0.001329	-0.000388	0.001179	-0.000790	-0.000304	-0.000014	0.000510	-0.002727
SK_ID_BUREAU	0.000135	1.000000	0.013015	-0.002628	0.009107	0.017890	0.002290	-0.000740	0.007962	0.005732	-0.003986	-0.000499	0.019398	0.001799
DAYS_CREDIT	0.000266	0.013015	1.000000	-0.027266	0.225682	0.875359	-0.014724	-0.030460	0.050883	0.135397	0.025140	-0.000383	0.688771	0.005676
CREDIT_DAY_OVERDUE	0.000283	-0.002628	-0.027266	1.000000	-0.007352	-0.008637	0.001249	0.002756	-0.003292	-0.002355	-0.000345	0.090951	-0.018461	-0.000339
DAYS_CREDIT_ENDDATE	0.000456	0.009107	0.225682	-0.007352	1.000000	0.248825	0.000577	0.113683	0.055424	0.081298	0.095421	0.001077	0.248525	0.000475
DAYS_ENDDATE_FACT	-0.000648	0.017890	0.875359	-0.008637	0.248825	1.000000	0.000999	0.012017	0.059096	0.019609	0.019476	-0.000332	0.751294	0.006274
AMT_CREDIT_MAX_OVERDUE	0.001329	0.002290	-0.014724	0.001249	0.000577	0.000999	1.000000	0.001523	0.081663	0.014007	-0.000112	0.015036	-0.000749	0.001578
CNT_CREDIT_PROLONG	-0.000388	-0.000740	-0.030460	0.002756	0.113683	0.012017	0.001523	1.000000	-0.008345	-0.001366	0.073805	0.000002	0.017864	-0.000465
AMT_CREDIT_SUM	0.001179	0.007962	0.050883	-0.003292	0.055424	0.059096	0.081663	-0.008345	1.000000	0.683419	0.003756	0.006342	0.104629	0.049146
AMT_CREDIT_SUM_DEBT	-0.000790	0.005732	0.135397	-0.002355	0.081298	0.019609	0.014007	-0.001366	0.683419	1.000000	-0.018215	0.008046	0.141235	0.025507
AMT_CREDIT_SUM_LIMIT	-0.000304	-0.003986	0.025140	-0.000345	0.095421	0.019476	-0.000112	0.073805	0.003756	-0.018215	1.000000	-0.000687	0.046028	0.004392
AMT_CREDIT_SUM_OVERDUE	-0.000014	-0.000499	-0.000383	0.090951	0.001077	-0.000332	0.015036	0.000002	0.006342	0.008046	-0.000687	1.000000	0.003528	0.000344
DAYS_CREDIT_UPDATE	0.000510	0.019398	0.688771	-0.018461	0.248525	0.751294	-0.000749	0.017864	0.104629	0.141235	0.046028	0.003528	1.000000	0.008418
AMT_ANNUITY	-0.002727	0.001799	0.005676	-0.000339	0.000475	0.006274	0.001578	-0.000465	0.049146	0.025507	0.004392	0.000344	0.008418	1.000000

	Percent	Test Missing Count
AMT_ANNUITY	71.47	1226791
AMT_CREDIT_MAX_OVERDUE	65.51	1124488
DAYS_ENDDATE_FACT	36.92	633653
AMT_CREDIT_SUM_LIMIT	34.48	591780
AMT_CREDIT_SUM_DEBT	15.01	257669
DAYS_CREDIT_ENDDATE	6.15	105553
AMT_CREDIT_SUM	0.00	13
CREDIT_ACTIVE	0.00	0
CREDIT_CURRENCY	0.00	0
DAYS_CREDIT	0.00	0
CREDIT_DAY_OVERDUE	0.00	0
SK_ID_BUREAU	0.00	0
CNT_CREDIT_PROLONG	0.00	0
AMT_CREDIT_SUM_OVERDUE	0.00	0
CREDIT_TYPE	0.00	0
DAYS_CREDIT_UPDATE	0.00	0
SK_ID_CURR	0.00	0

	SK_ID_BUREAU	MONTHS_BALANCE
count	2.729992e+07	2.729992e+07
mean	6.036297e+06	-3.074169e+01
std	4.923489e+05	2.386451e+01
min	5.001709e+06	-9.600000e+01
25%	5.730933e+06	-4.600000e+01
50%	6.070821e+06	-2.500000e+01
75%	6.431951e+06	-1.100000e+01
max	6.842888e+06	0.000000e+00

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	CNT_INSTALMENT	CNT_INSTALMENT_FUTURE	SK_DPD	SK_DPD_DEF
count	3.829580e+06	3.829580e+06	3.829580e+06	3.823444e+06	3.823437e+06	3.829579e+06	3.829579e+06
mean	1.904375e+06	2.785338e+05	-3.214404e+01	1.956578e+01	1.283459e+01	4.358176e-01	7.258109e-02
std	5.355338e+05	1.027329e+05	2.549135e+01	1.380046e+01	1.273046e+01	1.744642e+01	1.541065e+00
min	1.000001e+06	1.000010e+05	-9.600000e+01	1.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
25%	1.435030e+06	1.896800e+05	-4.600000e+01	1.000000e+01	4.000000e+00	0.000000e+00	0.000000e+00
50%	1.898227e+06	2.788660e+05	-2.300000e+01	1.200000e+01	9.000000e+00	0.000000e+00	0.000000e+00
75%	2.369573e+06	3.676380e+05	-1.200000e+01	2.400000e+01	1.800000e+01	0.000000e+00	0.000000e+00
max	2.843499e+06	4.562550e+05	-1.000000e+00	9.200000e+01	8.500000e+01	3.006000e+03	4.190000e+02

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	CNT_INSTALMENT	CNT_INSTALMENT_FUTURE	SK_DPD	SK_DPD_DEF
SK_ID_PREV	1.000000	-0.000208	0.003497	0.003542	0.003431	0.000632	0.000186
SK_ID_CURR	-0.000208	1.000000	0.000430	0.000618	-0.000105	-0.000401	0.002109
MONTHS_BALANCE	0.003497	0.000430	1.000000	0.433006	0.351605	-0.010548	-0.027817
CNT_INSTALMENT	0.003542	0.000618	0.433006	1.000000	0.897199	-0.013366	-0.009263
CNT_INSTALMENT_FUTURE	0.003431	-0.000105	0.351605	0.897199	1.000000	-0.020738	-0.017952
SK_DPD	0.000632	-0.000401	-0.010548	-0.013366	-0.020738	1.000000	0.090650
SK_DPD_DEF	0.000186	0.002109	-0.027817	-0.009263	-0.017952	0.090650	1.000000

	Percent	Test Missing Count
CNT_INSTALMENT_FUTURE	0.16	6143
CNT_INSTALMENT	0.16	6136
NAME_CONTRACT_STATUS	0.00	1
SK_DPD	0.00	1
SK_DPD_DEF	0.00	1
SK_ID_PREV	0.00	0
SK_ID_CURR	0.00	0
MONTHS_BALANCE	0.00	0

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	AMT_BALANCE	AMT_CREDIT_LIMIT_ACTUAL	AMT_DRAWINGS_ATM_CURRENT	AMT_DRAWINGS_CURRENT	AMT_DRAWINGS_OTHER_CURRENT	AMT_DRAWINGS_POS_CURRENT	AMT_INST_MIN_REGULARITY	...	AMT_RECEIVABLE_PRINCIPAL	AMT_RECIVABLE	AMT_TOTAL_RECEIVABLE	CNT_DRAWINGS_ATM_CURRENT	CNT_DRAWINGS_CURRENT	CNT_DRAWINGS_OTHER_CURRENT	CNT_DRAWINGS_POS_CURRENT	CNT_INSTALMENT_MATURE_CUM	SK_DPD	SK_DPD_DEF
count	3.840312e+06	3.840312e+06	3.840312e+06	3.840312e+06	3.840312e+06	3.090496e+06	3.840312e+06	3.090496e+06	3.090496e+06	3.535076e+06	...	3.840312e+06	3.840312e+06	3.840312e+06	3.090496e+06	3.840312e+06	3.090496e+06	3.090496e+06	3.535076e+06	3.840312e+06	3.840312e+06
mean	1.904504e+06	2.783242e+05	-3.452192e+01	5.830016e+04	1.538080e+05	5.961325e+03	7.433388e+03	2.881696e+02	2.968805e+03	3.540204e+03	...	5.596588e+04	5.808881e+04	5.809829e+04	3.094490e-01	7.031439e-01	4.812496e-03	5.594791e-01	2.082508e+01	9.283667e+00	3.316220e-01
std	5.364695e+05	1.027045e+05	2.666775e+01	1.063070e+05	1.651457e+05	2.822569e+04	3.384608e+04	8.201989e+03	2.079689e+04	5.600154e+03	...	1.025336e+05	1.059654e+05	1.059718e+05	1.100401e+00	3.190347e+00	8.263861e-02	3.240649e+00	2.005149e+01	9.751570e+01	2.147923e+01
min	1.000018e+06	1.000060e+05	-9.600000e+01	-4.202502e+05	0.000000e+00	-6.827310e+03	-6.211620e+03	0.000000e+00	0.000000e+00	0.000000e+00	...	-4.233058e+05	-4.202502e+05	-4.202502e+05	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
25%	1.434385e+06	1.895170e+05	-5.500000e+01	0.000000e+00	4.500000e+04	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	4.000000e+00	0.000000e+00	0.000000e+00
50%	1.897122e+06	2.783960e+05	-2.800000e+01	0.000000e+00	1.125000e+05	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.500000e+01	0.000000e+00	0.000000e+00
75%	2.369328e+06	3.675800e+05	-1.100000e+01	8.904669e+04	1.800000e+05	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	6.633911e+03	...	8.535924e+04	8.889949e+04	8.891451e+04	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	3.200000e+01	0.000000e+00	0.000000e+00
max	2.843496e+06	4.562500e+05	-1.000000e+00	1.505902e+06	1.350000e+06	2.115000e+06	2.287098e+06	1.529847e+06	2.239274e+06	2.028820e+05	...	1.472317e+06	1.493338e+06	1.493338e+06	5.100000e+01	1.650000e+02	1.200000e+01	1.650000e+02	1.200000e+02	3.260000e+03	3.260000e+03

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	AMT_BALANCE	AMT_CREDIT_LIMIT_ACTUAL	AMT_DRAWINGS_ATM_CURRENT	AMT_DRAWINGS_CURRENT	AMT_DRAWINGS_OTHER_CURRENT	AMT_DRAWINGS_POS_CURRENT	AMT_INST_MIN_REGULARITY	...	AMT_RECEIVABLE_PRINCIPAL	AMT_RECIVABLE	AMT_TOTAL_RECEIVABLE	CNT_DRAWINGS_ATM_CURRENT	CNT_DRAWINGS_CURRENT	CNT_DRAWINGS_OTHER_CURRENT	CNT_DRAWINGS_POS_CURRENT	CNT_INSTALMENT_MATURE_CUM	SK_DPD	SK_DPD_DEF
SK_ID_PREV	1.000000	0.004723	0.003670	0.005046	0.006631	0.004342	0.002624	-0.000160	0.001721	0.006460	...	0.005140	0.005035	0.005032	0.002821	0.000367	-0.001412	0.000809	-0.007219	-0.001786	0.001973
SK_ID_CURR	0.004723	1.000000	0.001696	0.003510	0.005991	0.000814	0.000708	0.000958	-0.000786	0.003300	...	0.003589	0.003518	0.003524	0.002082	0.002654	-0.000131	0.002135	-0.000581	-0.000962	0.001519
MONTHS_BALANCE	0.003670	0.001696	1.000000	0.014558	0.199900	0.036802	0.065527	0.000405	0.118146	-0.087529	...	0.016266	0.013172	0.013084	0.002536	0.113321	-0.026192	0.160207	-0.008620	0.039434	0.001659
AMT_BALANCE	0.005046	0.003510	0.014558	1.000000	0.489386	0.283551	0.336965	0.065366	0.169449	0.896728	...	0.999720	0.999917	0.999897	0.309968	0.259184	0.046563	0.155553	0.005009	-0.046988	0.013009
AMT_CREDIT_LIMIT_ACTUAL	0.006631	0.005991	0.199900	0.489386	1.000000	0.247219	0.263093	0.050579	0.234976	0.467620	...	0.490445	0.488641	0.488598	0.221808	0.204237	0.030051	0.202868	-0.157269	-0.038791	-0.002236
AMT_DRAWINGS_ATM_CURRENT	0.004342	0.000814	0.036802	0.283551	0.247219	1.000000	0.800190	0.017899	0.078971	0.094824	...	0.280402	0.278290	0.278260	0.732907	0.298173	0.013254	0.076083	-0.103721	-0.022044	-0.003360
AMT_DRAWINGS_CURRENT	0.002624	0.000708	0.065527	0.336965	0.263093	0.800190	1.000000	0.236297	0.615591	0.124469	...	0.337117	0.332831	0.332796	0.594361	0.523016	0.140032	0.359001	-0.093491	-0.020606	-0.003137
AMT_DRAWINGS_OTHER_CURRENT	-0.000160	0.000958	0.000405	0.065366	0.050579	0.017899	0.236297	1.000000	0.007382	0.002158	...	0.066108	0.064929	0.064923	0.012008	0.021271	0.575295	0.004458	-0.023013	-0.003693	-0.000568
AMT_DRAWINGS_POS_CURRENT	0.001721	-0.000786	0.118146	0.169449	0.234976	0.078971	0.615591	0.007382	1.000000	0.063562	...	0.173745	0.168974	0.168950	0.072658	0.520123	0.007620	0.542556	-0.106813	-0.015040	-0.002384
AMT_INST_MIN_REGULARITY	0.006460	0.003300	-0.087529	0.896728	0.467620	0.094824	0.124469	0.002158	0.063562	1.000000	...	0.896030	0.897617	0.897587	0.170616	0.148262	0.014360	0.086729	0.064320	-0.061484	-0.005715
AMT_PAYMENT_CURRENT	0.003472	0.000127	0.076355	0.143934	0.308294	0.189075	0.337343	0.034577	0.321055	0.333909	...	0.143162	0.142389	0.142371	0.142935	0.223483	0.017246	0.195074	-0.079266	-0.030222	-0.004340
AMT_PAYMENT_TOTAL_CURRENT	0.001641	0.000784	0.035614	0.151349	0.226570	0.159186	0.305726	0.025123	0.301760	0.335201	...	0.149936	0.149926	0.149914	0.125655	0.217857	0.014041	0.183973	-0.023156	-0.022475	-0.003443
AMT_RECEIVABLE_PRINCIPAL	0.005140	0.003589	0.016266	0.999720	0.490445	0.280402	0.337117	0.066108	0.173745	0.896030	...	1.000000	0.999727	0.999702	0.302627	0.258848	0.046543	0.157723	0.003664	-0.048290	0.006780
AMT_RECIVABLE	0.005035	0.003518	0.013172	0.999917	0.488641	0.278290	0.332831	0.064929	0.168974	0.897617	...	0.999727	1.000000	0.999995	0.303571	0.256347	0.046118	0.154507	0.005935	-0.046434	0.015466
AMT_TOTAL_RECEIVABLE	0.005032	0.003524	0.013084	0.999897	0.488598	0.278260	0.332796	0.064923	0.168950	0.897587	...	0.999702	0.999995	1.000000	0.303542	0.256317	0.046113	0.154481	0.005959	-0.046047	0.017243
CNT_DRAWINGS_ATM_CURRENT	0.002821	0.002082	0.002536	0.309968	0.221808	0.732907	0.594361	0.012008	0.072658	0.170616	...	0.302627	0.303571	0.303542	1.000000	0.410907	0.012730	0.108388	-0.103403	-0.029395	-0.004277
CNT_DRAWINGS_CURRENT	0.000367	0.002654	0.113321	0.259184	0.204237	0.298173	0.523016	0.021271	0.520123	0.148262	...	0.258848	0.256347	0.256317	0.410907	1.000000	0.033940	0.950546	-0.099186	-0.020786	-0.003106
CNT_DRAWINGS_OTHER_CURRENT	-0.001412	-0.000131	-0.026192	0.046563	0.030051	0.013254	0.140032	0.575295	0.007620	0.014360	...	0.046543	0.046118	0.046113	0.012730	0.033940	1.000000	0.007203	-0.021632	-0.006083	-0.000895
CNT_DRAWINGS_POS_CURRENT	0.000809	0.002135	0.160207	0.155553	0.202868	0.076083	0.359001	0.004458	0.542556	0.086729	...	0.157723	0.154507	0.154481	0.108388	0.950546	0.007203	1.000000	-0.129338	-0.018212	-0.002840
CNT_INSTALMENT_MATURE_CUM	-0.007219	-0.000581	-0.008620	0.005009	-0.157269	-0.103721	-0.093491	-0.023013	-0.106813	0.064320	...	0.003664	0.005935	0.005959	-0.103403	-0.099186	-0.021632	-0.129338	1.000000	0.059654	0.002156
SK_DPD	-0.001786	-0.000962	0.039434	-0.046988	-0.038791	-0.022044	-0.020606	-0.003693	-0.015040	-0.061484	...	-0.048290	-0.046434	-0.046047	-0.029395	-0.020786	-0.006083	-0.018212	0.059654	1.000000	0.218950
SK_DPD_DEF	0.001973	0.001519	0.001659	0.013009	-0.002236	-0.003360	-0.003137	-0.000568	-0.002384	-0.005715	...	0.006780	0.015466	0.017243	-0.004277	-0.003106	-0.000895	-0.002840	0.002156	0.218950	1.000000

	SK_ID_PREV	SK_ID_CURR	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	HOUR_APPR_PROCESS_START	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	...	RATE_INTEREST_PRIVILEGED	DAYS_DECISION	SELLERPLACE_AREA	CNT_PAYMENT	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
count	1.670214e+06	1.670214e+06	1.297979e+06	1.670214e+06	1.670213e+06	7.743700e+05	1.284699e+06	1.670214e+06	1.670214e+06	774370.000000	...	5951.000000	1.670214e+06	1.670214e+06	1.297984e+06	997149.000000	997149.000000	997149.000000	997149.000000	997149.000000	997149.000000
mean	1.923089e+06	2.783572e+05	1.595512e+04	1.752339e+05	1.961140e+05	6.697402e+03	2.278473e+05	1.248418e+01	9.964675e-01	0.079637	...	0.773503	-8.806797e+02	3.139511e+02	1.605408e+01	342209.855039	13826.269337	33767.774054	76582.403064	81992.343838	0.332570
std	5.325980e+05	1.028148e+05	1.478214e+04	2.927798e+05	3.185746e+05	2.092150e+04	3.153966e+05	3.334028e+00	5.932963e-02	0.107823	...	0.100879	7.790997e+02	7.127443e+03	1.456729e+01	88916.115833	72444.869708	106857.034789	149647.415123	153303.516729	0.471134
min	1.000001e+06	1.000010e+05	0.000000e+00	0.000000e+00	0.000000e+00	-9.000000e-01	0.000000e+00	0.000000e+00	0.000000e+00	-0.000015	...	0.373150	-2.922000e+03	-1.000000e+00	0.000000e+00	-2922.000000	-2892.000000	-2801.000000	-2889.000000	-2874.000000	0.000000
25%	1.461857e+06	1.893290e+05	6.321780e+03	1.872000e+04	2.416050e+04	0.000000e+00	5.084100e+04	1.000000e+01	1.000000e+00	0.000000	...	0.715645	-1.300000e+03	-1.000000e+00	6.000000e+00	365243.000000	-1628.000000	-1242.000000	-1314.000000	-1270.000000	0.000000
50%	1.923110e+06	2.787145e+05	1.125000e+04	7.104600e+04	8.054100e+04	1.638000e+03	1.123200e+05	1.200000e+01	1.000000e+00	0.051605	...	0.835095	-5.810000e+02	3.000000e+00	1.200000e+01	365243.000000	-831.000000	-361.000000	-537.000000	-499.000000	0.000000
75%	2.384280e+06	3.675140e+05	2.065842e+04	1.803600e+05	2.164185e+05	7.740000e+03	2.340000e+05	1.500000e+01	1.000000e+00	0.108909	...	0.852537	-2.800000e+02	8.200000e+01	2.400000e+01	365243.000000	-411.000000	129.000000	-74.000000	-44.000000	1.000000
max	2.845382e+06	4.562550e+05	4.180581e+05	6.905160e+06	6.905160e+06	3.060045e+06	6.905160e+06	2.300000e+01	1.000000e+00	1.000000	...	1.000000	-1.000000e+00	4.000000e+06	8.400000e+01	365243.000000	365243.000000	365243.000000	365243.000000	365243.000000	1.000000

	SK_ID_PREV	SK_ID_CURR	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	HOUR_APPR_PROCESS_START	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	...	RATE_INTEREST_PRIVILEGED	DAYS_DECISION	SELLERPLACE_AREA	CNT_PAYMENT	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
SK_ID_PREV	1.000000	-0.000321	0.011459	0.003302	0.003659	-0.001313	0.015293	-0.002652	-0.002828	-0.004051	...	-0.022312	0.019100	-0.001079	0.015589	-0.001478	-0.000071	0.001222	0.001915	0.001781	0.003986
SK_ID_CURR	-0.000321	1.000000	0.000577	0.000280	0.000195	-0.000063	0.000369	0.002842	0.000098	0.001158	...	-0.016757	-0.000637	0.001265	0.000031	-0.001329	-0.000757	0.000252	-0.000318	-0.000020	0.000876
AMT_ANNUITY	0.011459	0.000577	1.000000	0.808872	0.816429	0.267694	0.820895	-0.036201	0.020639	-0.103878	...	-0.202335	0.279051	-0.015027	0.394535	0.052839	-0.053295	-0.068877	0.082659	0.068022	0.283080
AMT_APPLICATION	0.003302	0.000280	0.808872	1.000000	0.975824	0.482776	0.999884	-0.014415	0.004310	-0.072479	...	-0.199733	0.133660	-0.007649	0.680630	0.074544	-0.049532	-0.084905	0.172627	0.148618	0.259219
AMT_CREDIT	0.003659	0.000195	0.816429	0.975824	1.000000	0.301284	0.993087	-0.021039	-0.025179	-0.188128	...	-0.205158	0.133763	-0.009567	0.674278	-0.036813	0.002881	0.044031	0.224829	0.214320	0.263932
AMT_DOWN_PAYMENT	-0.001313	-0.000063	0.267694	0.482776	0.301284	1.000000	0.482776	0.016776	0.001597	0.473935	...	-0.115343	-0.024536	0.003533	0.031659	-0.001773	-0.013586	-0.000869	-0.031425	-0.030702	-0.042585
AMT_GOODS_PRICE	0.015293	0.000369	0.820895	0.999884	0.993087	0.482776	1.000000	-0.045267	-0.017100	-0.072479	...	-0.199733	0.290422	-0.015842	0.672129	-0.024445	-0.021062	0.016883	0.211696	0.209296	0.243400
HOUR_APPR_PROCESS_START	-0.002652	0.002842	-0.036201	-0.014415	-0.021039	0.016776	-0.045267	1.000000	0.005789	0.025930	...	-0.045720	-0.039962	0.015671	-0.055511	0.014321	-0.002797	-0.016567	-0.018018	-0.018254	-0.117318
NFLAG_LAST_APPL_IN_DAY	-0.002828	0.000098	0.020639	0.004310	-0.025179	0.001597	-0.017100	0.005789	1.000000	0.004554	...	0.024640	0.016555	0.000912	0.063347	-0.000409	-0.002288	-0.001981	-0.002277	-0.000744	-0.007124
RATE_DOWN_PAYMENT	-0.004051	0.001158	-0.103878	-0.072479	-0.188128	0.473935	-0.072479	0.025930	0.004554	1.000000	...	-0.106143	-0.208742	-0.006489	-0.278875	-0.007969	-0.039178	-0.010934	-0.147562	-0.145461	-0.021633
RATE_INTEREST_PRIMARY	0.012969	0.033197	0.141823	0.110001	0.125106	0.016323	0.110001	-0.027172	0.009604	-0.103373	...	-0.001937	0.014037	0.159182	-0.019030	NaN	-0.017171	-0.000933	-0.010677	-0.011099	0.311938
RATE_INTEREST_PRIVILEGED	-0.022312	-0.016757	-0.202335	-0.199733	-0.205158	-0.115343	-0.199733	-0.045720	0.024640	-0.106143	...	1.000000	0.631940	-0.066316	-0.057150	NaN	0.150904	0.030513	0.372214	0.378671	-0.067157
DAYS_DECISION	0.019100	-0.000637	0.279051	0.133660	0.133763	-0.024536	0.290422	-0.039962	0.016555	-0.208742	...	0.631940	1.000000	-0.018382	0.246453	-0.012007	0.176711	0.089167	0.448549	0.400179	-0.028905
SELLERPLACE_AREA	-0.001079	0.001265	-0.015027	-0.007649	-0.009567	0.003533	-0.015842	0.015671	0.000912	-0.006489	...	-0.066316	-0.018382	1.000000	-0.010646	0.007401	-0.002166	-0.007510	-0.006291	-0.006675	-0.018280
CNT_PAYMENT	0.015589	0.000031	0.394535	0.680630	0.674278	0.031659	0.672129	-0.055511	0.063347	-0.278875	...	-0.057150	0.246453	-0.010646	1.000000	0.309900	-0.204907	-0.381013	0.088903	0.055121	0.320520
DAYS_FIRST_DRAWING	-0.001478	-0.001329	0.052839	0.074544	-0.036813	-0.001773	-0.024445	0.014321	-0.000409	-0.007969	...	NaN	-0.012007	0.007401	0.309900	1.000000	0.004710	-0.803494	-0.257466	-0.396284	0.177652
DAYS_FIRST_DUE	-0.000071	-0.000757	-0.053295	-0.049532	0.002881	-0.013586	-0.021062	-0.002797	-0.002288	-0.039178	...	0.150904	0.176711	-0.002166	-0.204907	0.004710	1.000000	0.513949	0.401838	0.323608	-0.119048
DAYS_LAST_DUE_1ST_VERSION	0.001222	0.000252	-0.068877	-0.084905	0.044031	-0.000869	0.016883	-0.016567	-0.001981	-0.010934	...	0.030513	0.089167	-0.007510	-0.381013	-0.803494	0.513949	1.000000	0.423462	0.493174	-0.221947
DAYS_LAST_DUE	0.001915	-0.000318	0.082659	0.172627	0.224829	-0.031425	0.211696	-0.018018	-0.002277	-0.147562	...	0.372214	0.448549	-0.006291	0.088903	-0.257466	0.401838	0.423462	1.000000	0.927990	0.012560
DAYS_TERMINATION	0.001781	-0.000020	0.068022	0.148618	0.214320	-0.030702	0.209296	-0.018254	-0.000744	-0.145461	...	0.378671	0.400179	-0.006675	0.055121	-0.396284	0.323608	0.493174	0.927990	1.000000	-0.003065
NFLAG_INSURED_ON_APPROVAL	0.003986	0.000876	0.283080	0.259219	0.263932	-0.042585	0.243400	-0.117318	-0.007124	-0.021633	...	-0.067157	-0.028905	-0.018280	0.320520	0.177652	-0.119048	-0.221947	0.012560	-0.003065	1.000000

	Percent	Test Missing Count
AMT_PAYMENT_CURRENT	20.00	767988
AMT_DRAWINGS_ATM_CURRENT	19.52	749816
CNT_DRAWINGS_POS_CURRENT	19.52	749816
AMT_DRAWINGS_OTHER_CURRENT	19.52	749816
AMT_DRAWINGS_POS_CURRENT	19.52	749816
CNT_DRAWINGS_OTHER_CURRENT	19.52	749816
CNT_DRAWINGS_ATM_CURRENT	19.52	749816
CNT_INSTALMENT_MATURE_CUM	7.95	305236
AMT_INST_MIN_REGULARITY	7.95	305236
SK_ID_PREV	0.00	0
AMT_TOTAL_RECEIVABLE	0.00	0
SK_DPD	0.00	0
NAME_CONTRACT_STATUS	0.00	0
CNT_DRAWINGS_CURRENT	0.00	0
AMT_PAYMENT_TOTAL_CURRENT	0.00	0
AMT_RECIVABLE	0.00	0
AMT_RECEIVABLE_PRINCIPAL	0.00	0
SK_ID_CURR	0.00	0
AMT_DRAWINGS_CURRENT	0.00	0
AMT_CREDIT_LIMIT_ACTUAL	0.00	0

	Percent	Test Missing Count
RATE_INTEREST_PRIVILEGED	99.64	1664263
RATE_INTEREST_PRIMARY	99.64	1664263
AMT_DOWN_PAYMENT	53.64	895844
RATE_DOWN_PAYMENT	53.64	895844
NAME_TYPE_SUITE	49.12	820405
NFLAG_INSURED_ON_APPROVAL	40.30	673065
DAYS_TERMINATION	40.30	673065
DAYS_LAST_DUE	40.30	673065
DAYS_LAST_DUE_1ST_VERSION	40.30	673065
DAYS_FIRST_DUE	40.30	673065
DAYS_FIRST_DRAWING	40.30	673065
AMT_GOODS_PRICE	23.08	385515
AMT_ANNUITY	22.29	372235
CNT_PAYMENT	22.29	372230
PRODUCT_COMBINATION	0.02	346
AMT_CREDIT	0.00	1
NAME_YIELD_GROUP	0.00	0
NAME_PORTFOLIO	0.00	0
NAME_SELLER_INDUSTRY	0.00	0
SELLERPLACE_AREA	0.00	0

	SK_ID_PREV	SK_ID_CURR	NUM_INSTALMENT_VERSION	NUM_INSTALMENT_NUMBER	DAYS_INSTALMENT	DAYS_ENTRY_PAYMENT	AMT_INSTALMENT	AMT_PAYMENT
count	1.360540e+07	1.360540e+07	1.360540e+07	1.360540e+07	1.360540e+07	1.360250e+07	1.360540e+07	1.360250e+07
mean	1.903365e+06	2.784449e+05	8.566373e-01	1.887090e+01	-1.042270e+03	-1.051114e+03	1.705091e+04	1.723822e+04
std	5.362029e+05	1.027183e+05	1.035216e+00	2.666407e+01	8.009463e+02	8.005859e+02	5.057025e+04	5.473578e+04
min	1.000001e+06	1.000010e+05	0.000000e+00	1.000000e+00	-2.922000e+03	-4.921000e+03	0.000000e+00	0.000000e+00
25%	1.434191e+06	1.896390e+05	0.000000e+00	4.000000e+00	-1.654000e+03	-1.662000e+03	4.226085e+03	3.398265e+03
50%	1.896520e+06	2.786850e+05	1.000000e+00	8.000000e+00	-8.180000e+02	-8.270000e+02	8.884080e+03	8.125515e+03
75%	2.369094e+06	3.675300e+05	1.000000e+00	1.900000e+01	-3.610000e+02	-3.700000e+02	1.671021e+04	1.610842e+04
max	2.843499e+06	4.562550e+05	1.780000e+02	2.770000e+02	-1.000000e+00	-1.000000e+00	3.771488e+06	3.771488e+06

	SK_ID_PREV	SK_ID_CURR	NUM_INSTALMENT_VERSION	NUM_INSTALMENT_NUMBER	DAYS_INSTALMENT	DAYS_ENTRY_PAYMENT	AMT_INSTALMENT	AMT_PAYMENT
SK_ID_PREV	1.000000	0.002132	0.000685	-0.002095	0.003748	0.003734	0.002042	0.001887
SK_ID_CURR	0.002132	1.000000	0.000480	-0.000548	0.001191	0.001215	-0.000226	-0.000124
NUM_INSTALMENT_VERSION	0.000685	0.000480	1.000000	-0.323414	0.130244	0.128124	0.168109	0.177176
NUM_INSTALMENT_NUMBER	-0.002095	-0.000548	-0.323414	1.000000	0.090286	0.094305	-0.089640	-0.087664
DAYS_INSTALMENT	0.003748	0.001191	0.130244	0.090286	1.000000	0.999491	0.125985	0.127018
DAYS_ENTRY_PAYMENT	0.003734	0.001215	0.128124	0.094305	0.999491	1.000000	0.125555	0.126602
AMT_INSTALMENT	0.002042	-0.000226	0.168109	-0.089640	0.125985	0.125555	1.000000	0.937191
AMT_PAYMENT	0.001887	-0.000124	0.177176	-0.087664	0.127018	0.126602	0.937191	1.000000

	Percent	Test Missing Count
DAYS_ENTRY_PAYMENT	0.02	2905
AMT_PAYMENT	0.02	2905
SK_ID_PREV	0.00	0
SK_ID_CURR	0.00	0
NUM_INSTALMENT_VERSION	0.00	0
NUM_INSTALMENT_NUMBER	0.00	0
DAYS_INSTALMENT	0.00	0
AMT_INSTALMENT	0.00	0

	SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	...	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
0	100002	1	Cash loans	M	N	Y	202500.0	406597.5	24700.5	...	0.0	0.0	0.0	0.0	0.0	1.0
1	100003	0	Cash loans	F	N	N	270000.0	1293502.5	35698.5	...	0.0	0.0	0.0	0.0	0.0	0.0
2	100004	0	Revolving loans	M	Y	Y	67500.0	135000.0	6750.0	...	0.0	0.0	0.0	0.0	0.0	0.0
3	100006	0	Cash loans	F	N	Y	135000.0	312682.5	29686.5	...	NaN	NaN	NaN	NaN	NaN	NaN
4	100007	0	Cash loans	M	N	Y	121500.0	513000.0	21865.5	...	0.0	0.0	0.0	0.0	0.0	0.0

	SK_ID_CURR	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	...	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
0	100001	Cash loans	F	N	Y	0	135000.0	568800.0	20560.5	450000.0	...	0.0	0.0	0.0	0.0	0.0	0.0
1	100005	Cash loans	M	N	Y	0	99000.0	222768.0	17370.0	180000.0	...	0.0	0.0	0.0	0.0	0.0	3.0
2	100013	Cash loans	M	Y	Y	0	202500.0	663264.0	69777.0	630000.0	...	0.0	0.0	0.0	0.0	1.0	4.0
3	100028	Cash loans	F	N	Y	2	315000.0	1575000.0	49018.5	1575000.0	...	0.0	0.0	0.0	0.0	0.0	3.0
4	100038	Cash loans	M	Y	N	1	180000.0	625500.0	32067.0	625500.0	...	NaN	NaN	NaN	NaN	NaN	NaN

	SK_ID_CURR	SK_ID_BUREAU	CREDIT_ACTIVE	CREDIT_CURRENCY	DAYS_CREDIT	DAYS_CREDIT_ENDDATE	DAYS_ENDDATE_FACT	AMT_CREDIT_MAX_OVERDUE	AMT_CREDIT_SUM	AMT_CREDIT_SUM_DEBT	AMT_CREDIT_SUM_LIMIT	CREDIT_TYPE	DAYS_CREDIT_UPDATE	AMT_ANNUITY
0	215354	5714462	Closed	currency 1	-497	-153.0	-153.0	NaN	91323.0	0.0	NaN	Consumer credit	-131	NaN
1	215354	5714463	Active	currency 1	-208	1075.0	NaN	NaN	225000.0	171342.0	NaN	Credit card	-20	NaN
2	215354	5714464	Active	currency 1	-203	528.0	NaN	NaN	464323.5	NaN	NaN	Consumer credit	-16	NaN
3	215354	5714465	Active	currency 1	-203	NaN	NaN	NaN	90000.0	NaN	NaN	Credit card	-16	NaN
4	215354	5714466	Active	currency 1	-629	1197.0	NaN	77674.5	2700000.0	NaN	NaN	Consumer credit	-21	NaN

	SK_ID_PREV	SK_ID_CURR	NAME_CONTRACT_TYPE	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	...	NAME_SELLER_INDUSTRY	CNT_PAYMENT	NAME_YIELD_GROUP	PRODUCT_COMBINATION	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
0	2030495	271877	Consumer loans	1730.430	17145.0	17145.0	0.0	17145.0	SATURDAY	15	...	Connectivity	12.0	middle	POS mobile with interest	365243.0	-42.0	300.0	-42.0	-37.0	0.0
1	2802425	108129	Cash loans	25188.615	607500.0	679671.0	NaN	607500.0	THURSDAY	11	...	XNA	36.0	low_action	Cash X-Sell: low	365243.0	-134.0	916.0	365243.0	365243.0	1.0
2	2523466	122040	Cash loans	15060.735	112500.0	136444.5	NaN	112500.0	TUESDAY	11	...	XNA	12.0	high	Cash X-Sell: high	365243.0	-271.0	59.0	365243.0	365243.0	1.0
3	2819243	176158	Cash loans	47041.335	450000.0	470790.0	NaN	450000.0	MONDAY	7	...	XNA	12.0	middle	Cash X-Sell: middle	365243.0	-482.0	-152.0	-182.0	-177.0	1.0
4	1784265	202054	Cash loans	31924.395	337500.0	404055.0	NaN	337500.0	THURSDAY	9	...	XNA	24.0	high	Cash Street: high	NaN	NaN	NaN	NaN	NaN	NaN

	SK_ID_PREV	SK_ID_CURR	NUM_INSTALMENT_VERSION	NUM_INSTALMENT_NUMBER	DAYS_INSTALMENT	DAYS_ENTRY_PAYMENT	AMT_INSTALMENT	AMT_PAYMENT
0	1054186	161674	1.0	6	-1180.0	-1187.0	6948.360	6948.360
1	1330831	151639	0.0	34	-2156.0	-2156.0	1716.525	1716.525
2	2085231	193053	2.0	1	-63.0	-63.0	25425.000	25425.000
3	2452527	199697	1.0	3	-2418.0	-2426.0	24350.130	24350.130
4	2714724	167756	1.0	2	-1383.0	-1366.0	2165.040	2160.585

Project Title: Home Credit Default Risk (HCDR)¶

Some of the challenges¶

Back ground Home Credit Group¶

Project Title: Home Credit Default Risk (HCDR)¶

Exploratory Data Analysis¶

Dataset Size¶

Function to plot the missing values¶

Summary of Application train¶

Missing values in Application Train¶

Summary of Application Test¶

Missing data for Application Test¶

Summary of Bureau¶

Missing data for Bureau¶

Summary of Bureau Balance¶

Missing data for Bureau Balance¶

Summary of POS_CASH_balance¶

Missing data for POS_CASH_balance¶

Summary of credit_card_balance¶

Missing data for credit_card_balance¶

Summary of previous_application¶

Missing data for previous_application¶

Summary of installments_payments¶

Missing data for installments_payments¶

Exploratory Data Analysis¶

Dataset Size¶

Function to plot the missing values¶

Summary of Application train¶

Missing values in Application Train¶

Summary of Application Test¶

Missing data for Application Test¶

Summary of Bureau¶

Missing data for Bureau¶

Summary of Bureau Balance¶

Missing data for Bureau Balance¶

Summary of POS_CASH_balance¶

Missing data for POS_CASH_balance¶

Summary of credit_card_balance¶

Missing data for credit_card_balance¶

Summary of previous_application¶

Missing data for previous_application¶

Summary of installments_payments¶

Missing data for installments_payments¶

Phase 4: Multi Layer Perceptron Models (Ran on Google Colab and on a mac)¶

Imports¶

FEATURE ENGINEERING (Carryover from phase 3 )¶

Undersampling - Due to class imbalance¶

From Phase 3¶

Loading Datasets and Constructing Pipeline¶

Evaluation metrics¶

ACCURACY¶

PRECISION:¶

RECALL¶

F1 SCORE¶

AUC¶

Loss Function Used¶

Modules for Training and Testing¶

Model Pipeline¶

Block diagram of pipeline¶

Model- 1 SIMPLE MLP¶

Experiment1: All features before feature selection¶

Experiment2: Selected features after x>0 from findings we did in phase 3¶

Model-2¶

Experiment1: All Features¶

Experiment2¶

Experiment3: Selected features after x>0 from findings we did in phase 3¶

Experiment4: Experiment3 by changing learning rate and epochs¶

Model3¶

Experiment1 - All features¶

Experiment2: Selected features¶

Hyper Parameter Tuning¶

Final Experiment Result Table¶

GAP Analysis:¶

Submission File Prep¶

Kaggle submission via the command line API¶

report submission¶

Write-up¶

Project Title: Home Credit Default Risk¶

Team and Phase leader plan¶

Credit Assignment Plan¶

Abstract¶

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	AMT_BALANCE	AMT_CREDIT_LIMIT_ACTUAL	AMT_DRAWINGS_ATM_CURRENT	AMT_DRAWINGS_CURRENT	AMT_DRAWINGS_POS_CURRENT	AMT_INST_MIN_REGULARITY	...	AMT_RECIVABLE	AMT_TOTAL_RECEIVABLE	CNT_DRAWINGS_ATM_CURRENT	CNT_DRAWINGS_CURRENT	CNT_DRAWINGS_POS_CURRENT	CNT_INSTALMENT_MATURE_CUM	NAME_CONTRACT_STATUS
0	2562384	378907	-6	56.970	135000	0.0	877.5	877.5	1700.325	...	0.000	0.000	0.0	1	1.0	35.0	Active
1	2582071	363914	-1	63975.555	45000	2250.0	2250.0	0.0	2250.000	...	64875.555	64875.555	1.0	1	0.0	69.0	Active
2	1740877	371185	-7	31815.225	450000	0.0	0.0	0.0	2250.000	...	31460.085	31460.085	0.0	0	0.0	30.0	Active
3	1389973	337855	-4	236572.110	225000	2250.0	2250.0	0.0	11795.760	...	233048.970	233048.970	1.0	1	0.0	10.0	Active
4	1891521	126868	-1	453919.455	450000	0.0	11547.0	11547.0	22924.890	...	453919.455	453919.455	0.0	1	1.0	101.0	Active

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	CNT_INSTALMENT	CNT_INSTALMENT_FUTURE	NAME_CONTRACT_STATUS
0	1803195	182943	-31	48.0	45.0	Active
1	1715348	367990	-33	36.0	35.0	Active
2	1784872	397406	-32	12.0	9.0	Active
3	1903291	269225	-35	48.0	42.0	Active
4	2341044	334279	-35	36.0	35.0	Active

	exp_name	learning_rate	epochs	Train Time (sec)	Test Time (sec)	Train Acc	Test Acc	Train AUC	Test AUC	Train F1	Test F1
0	Model1 All	0.01	1000.0	5.0025	3.6912	0.6909	0.6828	0.6909	0.6828	0.6903	0.6832
1	Model1 All	0.01	1000.0	5.0025	3.6912	0.6909	0.6828	0.6909	0.6828	0.6903	0.6832
2	Model1 All	0.01	1000.0	5.0025	3.6912	0.6909	0.6828	0.6909	0.6828	0.6903	0.6832

	exp_name	learning_rate	epochs	Train Time (sec)	Test Time (sec)	Train Acc	Test Acc	Train AUC	Test AUC	Train F1	Test F1
0	Model1 All	0.010	1000.0	5.0025	3.6912	0.6909	0.6828	0.6909	0.6828	0.6903	0.6832
1	Model1 All	0.010	1000.0	5.0025	3.6912	0.6909	0.6828	0.6909	0.6828	0.6903	0.6832
2	Model1 All	0.010	1000.0	5.0025	3.6912	0.6909	0.6828	0.6909	0.6828	0.6903	0.6832
3	Model1 selected	0.010	1000.0	2.2970	2.2334	0.6902	0.6814	0.6902	0.6814	0.6896	0.6816
4	Model 2 Enhanced all	0.010	1000.0	27.0990	28.2518	0.9990	0.6346	0.9990	0.6349	0.9990	0.6661
5	Model 2 enhanced 2	0.001	50.0	1.4786	1.4070	0.7411	0.6806	0.7411	0.6807	0.7501	0.6925

	exp_name	learning_rate	epochs	Train Time (sec)	Test Time (sec)	Train Acc	Test Acc	Train AUC	Test AUC	Train F1	Test F1
0	Model1 All	0.0100	1000.0	5.0025	3.6912	0.6909	0.6828	0.6909	0.6828	0.6903	0.6832
1	Model1 All	0.0100	1000.0	5.0025	3.6912	0.6909	0.6828	0.6909	0.6828	0.6903	0.6832
2	Model1 All	0.0100	1000.0	5.0025	3.6912	0.6909	0.6828	0.6909	0.6828	0.6903	0.6832
3	Model1 selected	0.0100	1000.0	2.2970	2.2334	0.6902	0.6814	0.6902	0.6814	0.6896	0.6816
4	Model 2 Enhanced all	0.0100	1000.0	27.0990	28.2518	0.9990	0.6346	0.9990	0.6349	0.9990	0.6661
5	Model 2 enhanced 2	0.0010	50.0	1.4786	1.4070	0.7411	0.6806	0.7411	0.6807	0.7501	0.6925
6	Model 2 enhanced and selected	0.0010	50.0	1.4156	1.4354	0.7364	0.6826	0.7364	0.6826	0.7413	0.6904
7	Model 3 change learning rate and epochs and se...	0.0005	50.0	1.4849	1.4059	0.7101	0.6816	0.7101	0.6817	0.7165	0.6915

S. No	Table Name	Rows	Features	Numerical Features	Categorical Features	Megabytes
1	application_train	307,511	122	106	16	158MB
2	application_test	48,744	121	105	16	25MB
3	bureau	1,716,428	17	14	3	162MB
4	bureau_balance	27,299,925	3	2	1	358MB
5	credit_card_balance	3,840,312	23	22	1	405MB
6	installments_payments	13,605,401	8	21	16	690MB
7	previous_application	1,670,214	37	8	0	386MB
8	POS_CASH_balance	10,001,358	8	7	1	375MB